From 12954c2964f70e7fa952584196eb813719df8604 Mon Sep 17 00:00:00 2001
From: ViniFBN <81927867+ViniFBN@users.noreply.github.com>
Date: Mon, 16 Dec 2024 13:58:00 +0100
Subject: [PATCH 01/63] Added module 3 to translate

copying the files in order to translate them later
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 .../README.md                                 |   39 +
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 .../lora_adapters.md                          |  124 +
 .../notebooks/finetune_sft_peft.ipynb         |  516 ++
 .../notebooks/load_lora_adapter.ipynb         | 5061 +++++++++++++++++
 .../prompt_tuning.md                          |   74 +
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 create mode 100644 pt-br/3_parameter_efficient_finetuning/lora_adapters.md
 create mode 100644 pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
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diff --git a/pt-br/3_parameter_efficient_finetuning/README.md b/pt-br/3_parameter_efficient_finetuning/README.md
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+# Parameter-Efficient Fine-Tuning (PEFT)
+
+As language models grow larger, traditional fine-tuning becomes increasingly challenging. A full fine-tuning of even a 1.7B parameter model requires substantial GPU memory, makes storing separate model copies expensive, and risks catastrophic forgetting of the model's original capabilities. Parameter-efficient fine-tuning (PEFT) methods address these challenges by modifying only a small subset of model parameters while keeping most of the model frozen.
+
+Traditional fine-tuning updates all model parameters during training, which becomes impractical for large models. PEFT methods introduce approaches to adapt models using fewer trainable parameters - often less than 1% of the original model size. This dramatic reduction in trainable parameters enables:
+
+- Fine-tuning on consumer hardware with limited GPU memory
+- Storing multiple task-specific adaptations efficiently
+- Better generalization in low-data scenarios
+- Faster training and iteration cycles
+
+## Available Methods
+
+In this module, we will cover two popular PEFT methods:
+
+### 1️⃣ LoRA (Low-Rank Adaptation)
+
+LoRA has emerged as the most widely adopted PEFT method, offering an elegant solution to efficient model adaptation. Instead of modifying the entire model, **LoRA injects trainable matrices into the model's attention layers.** This approach typically reduces trainable parameters by about 90% while maintaining comparable performance to full fine-tuning. We will explore LoRA in the [LoRA (Low-Rank Adaptation)](./lora_adapters.md) section.
+ 
+### 2️⃣ Prompt Tuning
+
+Prompt tuning offers an **even lighter** approach by **adding trainable tokens to the input** rather than modifying model weights. Prompt tuning is less popular than LoRA, but can be a useful technique for quickly adapting a model to new tasks or domains. We will explore prompt tuning in the [Prompt Tuning](./prompt_tuning.md) section.
+
+## Exercise Notebooks
+
+| Title | Description | Exercise | Link | Colab |
+|-------|-------------|----------|------|-------|
+| LoRA Fine-tuning | Learn how to fine-tune models using LoRA adapters | 🐢 Train a model using LoRA<br>🐕 Experiment with different rank values<br>🦁 Compare performance with full fine-tuning | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| Load LoRA Adapters | Learn how to load and use trained LoRA adapters | 🐢 Load pre-trained adapters<br>🐕 Merge adapters with base model<br>🦁 Switch between multiple adapters | [Notebook](./notebooks/load_lora_adapter_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+<!-- | Prompt Tuning | Learn how to implement prompt tuning | 🐢 Train soft prompts<br>🐕 Compare different initialization strategies<br>🦁 Evaluate on multiple tasks | [Notebook](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
+
+## Resources
+- [PEFT Documentation](https://huggingface.co/docs/peft)
+- [LoRA Paper](https://arxiv.org/abs/2106.09685)
+- [QLoRA Paper](https://arxiv.org/abs/2305.14314)
+- [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691)
+- [Hugging Face PEFT Guide](https://huggingface.co/blog/peft)
+- [How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
+- [TRL](https://huggingface.co/docs/trl/index)
\ No newline at end of file
diff --git a/pt-br/3_parameter_efficient_finetuning/images/lora_adapter.png b/pt-br/3_parameter_efficient_finetuning/images/lora_adapter.png
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diff --git a/pt-br/3_parameter_efficient_finetuning/lora_adapters.md b/pt-br/3_parameter_efficient_finetuning/lora_adapters.md
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+# LoRA (Low-Rank Adaptation)
+
+LoRA has become the most widely adopted PEFT method. It works by adding small rank decomposition matrices to the attention weights, typically reducing trainable parameters by about 90%. 
+
+## Understanding LoRA
+
+LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that freezes the pre-trained model weights and injects trainable rank decomposition matrices into the model's layers. Instead of training all model parameters during fine-tuning, LoRA decomposes the weight updates into smaller matrices through low-rank decomposition, significantly reducing the number of trainable parameters while maintaining model performance. For example, when applied to GPT-3 175B, LoRA reduced trainable parameters by 10,000x and GPU memory requirements by 3x compared to full fine-tuning. You can read more about LoRA in the [LoRA paper](https://arxiv.org/pdf/2106.09685).
+
+LoRA works by adding pairs of rank decomposition matrices to transformer layers, typically focusing on attention weights. During inference, these adapter weights can be merged with the base model, resulting in no additional latency overhead. LoRA is particularly useful for adapting large language models to specific tasks or domains while keeping resource requirements manageable.
+
+## Loading LoRA Adapters
+
+Adapters can be loaded onto a pretrained model with load_adapter(), which is useful for trying out different adapters whose weights aren’t merged. Set the active adapter weights with the set_adapter() function. To return the base model, you could use unload() to unload all of the LoRA modules. This makes it easy to switch between different task-specific weights.
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
+peft_model_id = "<peft_adapter_id>"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+```
+
+![lora_load_adapter](./images/lora_adapter.png)
+
+## Merging LoRA Adapters
+
+After training with LoRA, you might want to merge the adapter weights back into the base model for easier deployment. This creates a single model with the combined weights, eliminating the need to load adapters separately during inference.
+
+The merging process requires attention to memory management and precision. Since you'll need to load both the base model and adapter weights simultaneously, ensure sufficient GPU/CPU memory is available. Using `device_map="auto"` in `transformers` will help with automatic memory management. Maintain consistent precision (e.g., float16) throughout the process, matching the precision used during training and saving the merged model in the same format for deployment. Before deploying, always validate the merged model by comparing its outputs and performance metrics with the adapter-based version.
+
+Adapters are also be convenient for switching between different tasks or domains. You can load the base model and adapter weights separately. This allows for quick switching between different task-specific weights. 
+
+## Implementation Guide
+
+The `notebooks/` directory contains practical tutorials and exercises for implementing different PEFT methods. Begin with `load_lora_adapter_example.ipynb` for a basic introduction, then explore `lora_finetuning.ipynb` for a more detailed look at how to fine-tune a model with LoRA and SFT.
+
+When implementing PEFT methods, start with small rank values (4-8) for LoRA and monitor training loss. Use validation sets to prevent overfitting and compare results with full fine-tuning baselines when possible. The effectiveness of different methods can vary by task, so experimentation is key.
+
+## OLoRA
+
+[OLoRA](https://arxiv.org/abs/2406.01775) utilizes QR decomposition to initialize the LoRA adapters. OLoRA translates the base weights of the model by a factor of their QR decompositions, i.e., it mutates the weights before performing any training on them. This approach significantly improves stability, accelerates convergence speed, and ultimately achieves superior performance.
+
+## Using TRL with PEFT
+
+PEFT methods can be combined with TRL (Transformers Reinforcement Learning) for efficient fine-tuning. This integration is particularly useful for RLHF (Reinforcement Learning from Human Feedback) as it reduces memory requirements.
+
+```python
+from peft import LoraConfig
+from transformers import AutoModelForCausalLM
+
+# Load model with PEFT config
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# Load model on specific device
+model = AutoModelForCausalLM.from_pretrained(
+    "your-model-name",
+    load_in_8bit=True,  # Optional: use 8-bit precision
+    device_map="auto",
+    peft_config=lora_config
+)
+```
+
+Above, we used `device_map="auto"` to automatically assign the model to the correct device. You can also manually assign the model to a specific device using `device_map={"": device_index}`. You could also scale training across multiple GPUs while keeping memory usage efficient.
+
+## Basic Merging Implementation
+
+After training a LoRA adapter, you can merge the adapter weights back into the base model. Here's how to do it:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+# 1. Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "base_model_name",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# 2. Load the PEFT model with adapter
+peft_model = PeftModel.from_pretrained(
+    base_model,
+    "path/to/adapter",
+    torch_dtype=torch.float16
+)
+
+# 3. Merge adapter weights with base model
+try:
+    merged_model = peft_model.merge_and_unload()
+except RuntimeError as e:
+    print(f"Merging failed: {e}")
+    # Implement fallback strategy or memory optimization
+
+# 4. Save the merged model
+merged_model.save_pretrained("path/to/save/merged_model")
+```
+
+If you encounter size discrepancies in the saved model, ensure you're also saving the tokenizer:
+
+```python
+# Save both model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("base_model_name")
+merged_model.save_pretrained("path/to/save/merged_model")
+tokenizer.save_pretrained("path/to/save/merged_model")
+```
+
+## Next Steps
+
+⏩ Move on to the [Prompt Tuning](prompt_tuning.md) guide to learn how to fine-tune a model with prompt tuning.
+⏩ Move on the [Load LoRA Adapters Tutorial](./notebooks/load_lora_adapter.ipynb) to learn how to load LoRA adapters.
+
+# Resources
+
+- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
+- [PEFT Documentation](https://huggingface.co/docs/peft)
+- [Hugging Face blog post on PEFT](https://huggingface.co/blog/peft)
diff --git a/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z-6LLOPZouLg"
+   },
+   "source": [
+    "# How to Fine-Tune LLMs with LoRA Adapters using Hugging Face TRL\n",
+    "\n",
+    "This notebook demonstrates how to efficiently fine-tune large language models using LoRA (Low-Rank Adaptation) adapters. LoRA is a parameter-efficient fine-tuning technique that:\n",
+    "- Freezes the pre-trained model weights\n",
+    "- Adds small trainable rank decomposition matrices to attention layers\n",
+    "- Typically reduces trainable parameters by ~90%\n",
+    "- Maintains model performance while being memory efficient\n",
+    "\n",
+    "We'll cover:\n",
+    "1. Setup development environment and LoRA configuration\n",
+    "2. Create and prepare the dataset for adapter training\n",
+    "3. Fine-tune using `trl` and `SFTTrainer` with LoRA adapters\n",
+    "4. Test the model and merge adapters (optional)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fXqd9BXgouLi"
+   },
+   "source": [
+    "## 1. Setup development environment\n",
+    "\n",
+    "Our first step is to install Hugging Face Libraries and Pyroch, including trl, transformers and datasets. If you haven't heard of trl yet, don't worry. It is a new library on top of transformers and datasets, which makes it easier to fine-tune, rlhf, align open LLMs.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tKvGVxImouLi"
+   },
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XHUzfwpKouLk"
+   },
+   "source": [
+    "## 2. Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 2260\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 119\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load a sample dataset\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: define your dataset and config using the path and name parameters\n",
+    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TOhJdtsouLk"
+   },
+   "source": [
+    "## 3. Fine-tune LLM using `trl` and the `SFTTrainer` with LoRA\n",
+    "\n",
+    "The [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) from `trl` provides integration with LoRA adapters through the [PEFT](https://huggingface.co/docs/peft/en/index) library. Key advantages of this setup include:\n",
+    "\n",
+    "1. **Memory Efficiency**: \n",
+    "   - Only adapter parameters are stored in GPU memory\n",
+    "   - Base model weights remain frozen and can be loaded in lower precision\n",
+    "   - Enables fine-tuning of large models on consumer GPUs\n",
+    "\n",
+    "2. **Training Features**:\n",
+    "   - Native PEFT/LoRA integration with minimal setup\n",
+    "   - Support for QLoRA (Quantized LoRA) for even better memory efficiency\n",
+    "\n",
+    "3. **Adapter Management**:\n",
+    "   - Adapter weight saving during checkpoints\n",
+    "   - Features to merge adapters back into base model\n",
+    "\n",
+    "We'll use LoRA in our example, which combines LoRA with 4-bit quantization to further reduce memory usage without sacrificing performance. The setup requires just a few configuration steps:\n",
+    "1. Define the LoRA configuration (rank, alpha, dropout)\n",
+    "2. Create the SFTTrainer with PEFT config\n",
+    "3. Train and save the adapter weights\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Load the model and tokenizer\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# Set up the chat format\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZbuVArTHouLk"
+   },
+   "source": [
+    "The `SFTTrainer`  supports a native integration with `peft`, which makes it super easy to efficiently tune LLMs using, e.g. LoRA. We only need to create our `LoraConfig` and provide it to the trainer.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercise: Define LoRA parameters for finetuning</h2>\n",
+    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
+    "    <p><b>Difficulty Levels</b></p>\n",
+    "    <p>🐢 Use the general parameters for an abitrary finetune</p>\n",
+    "    <p>🐕 Adjust the parameters and review in weights & biases.</p>\n",
+    "    <p>🦁 Adjust the parameters and show change in inference results.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "blDSs9swouLk"
+   },
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig\n",
+    "\n",
+    "# TODO: Configure LoRA parameters\n",
+    "# r: rank dimension for LoRA update matrices (smaller = more compression)\n",
+    "rank_dimension = 6\n",
+    "# lora_alpha: scaling factor for LoRA layers (higher = stronger adaptation)\n",
+    "lora_alpha = 8\n",
+    "# lora_dropout: dropout probability for LoRA layers (helps prevent overfitting)\n",
+    "lora_dropout = 0.05\n",
+    "\n",
+    "peft_config = LoraConfig(\n",
+    "    r=rank_dimension,  # Rank dimension - typically between 4-32\n",
+    "    lora_alpha=lora_alpha,  # LoRA scaling factor - typically 2x rank\n",
+    "    lora_dropout=lora_dropout,  # Dropout probability for LoRA layers\n",
+    "    bias=\"none\",  # Bias type for LoRA. the corresponding biases will be updated during training.\n",
+    "    target_modules=\"all-linear\",  # Which modules to apply LoRA to\n",
+    "    task_type=\"CAUSAL_LM\",  # Task type for model architecture\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l5NUDPcaouLl"
+   },
+   "source": [
+    "Before we can start our training we need to define the hyperparameters (`TrainingArguments`) we want to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NqT28VZlouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# Training configuration\n",
+    "# Hyperparameters based on QLoRA paper recommendations\n",
+    "args = SFTConfig(\n",
+    "    # Output settings\n",
+    "    output_dir=finetune_name,  # Directory to save model checkpoints\n",
+    "    # Training duration\n",
+    "    num_train_epochs=1,  # Number of training epochs\n",
+    "    # Batch size settings\n",
+    "    per_device_train_batch_size=2,  # Batch size per GPU\n",
+    "    gradient_accumulation_steps=2,  # Accumulate gradients for larger effective batch\n",
+    "    # Memory optimization\n",
+    "    gradient_checkpointing=True,  # Trade compute for memory savings\n",
+    "    # Optimizer settings\n",
+    "    optim=\"adamw_torch_fused\",  # Use fused AdamW for efficiency\n",
+    "    learning_rate=2e-4,  # Learning rate (QLoRA paper)\n",
+    "    max_grad_norm=0.3,  # Gradient clipping threshold\n",
+    "    # Learning rate schedule\n",
+    "    warmup_ratio=0.03,  # Portion of steps for warmup\n",
+    "    lr_scheduler_type=\"constant\",  # Keep learning rate constant after warmup\n",
+    "    # Logging and saving\n",
+    "    logging_steps=10,  # Log metrics every N steps\n",
+    "    save_strategy=\"epoch\",  # Save checkpoint every epoch\n",
+    "    # Precision settings\n",
+    "    bf16=True,  # Use bfloat16 precision\n",
+    "    # Integration settings\n",
+    "    push_to_hub=False,  # Don't push to HuggingFace Hub\n",
+    "    report_to=None,  # Disable external logging\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cGhR7uFBouLl"
+   },
+   "source": [
+    "We now have every building block we need to create our `SFTTrainer` to start then training our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "M00Har2douLl"
+   },
+   "outputs": [],
+   "source": [
+    "max_seq_length = 1512  # max sequence length for model and packing of the dataset\n",
+    "\n",
+    "# Create SFTTrainer with LoRA configuration\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    peft_config=peft_config,  # LoRA configuration\n",
+    "    max_seq_length=max_seq_length,  # Maximum sequence length\n",
+    "    tokenizer=tokenizer,\n",
+    "    packing=True,  # Enable input packing for efficiency\n",
+    "    dataset_kwargs={\n",
+    "        \"add_special_tokens\": False,  # Special tokens handled by template\n",
+    "        \"append_concat_token\": False,  # No additional separator needed\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zQ_kRN24ouLl"
+   },
+   "source": [
+    "Start training our model by calling the `train()` method on our `Trainer` instance. This will start the training loop and train our model for 3 epochs. Since we are using a PEFT method, we will only save the adapted model weights and not the full model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Tq4nIYqKouLl"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "300e5dfbb4b54750b77324345c7591f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/72 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# start training, the model will be automatically saved to the hub and the output directory\n",
+    "trainer.train()\n",
+    "\n",
+    "# save model\n",
+    "trainer.save_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y4HHSYYzouLl"
+   },
+   "source": [
+    "The training with Flash Attention for 3 epochs with a dataset of 15k samples took 4:14:36 on a `g5.2xlarge`. The instance costs `1.21$/h` which brings us to a total cost of only ~`5.3$`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C309KsXjouLl"
+   },
+   "source": [
+    "### Merge LoRA Adapter into the Original Model\n",
+    "\n",
+    "When using LoRA, we only train adapter weights while keeping the base model frozen. During training, we save only these lightweight adapter weights (~2-10MB) rather than a full model copy. However, for deployment, you might want to merge the adapters back into the base model for:\n",
+    "\n",
+    "1. **Simplified Deployment**: Single model file instead of base model + adapters\n",
+    "2. **Inference Speed**: No adapter computation overhead\n",
+    "3. **Framework Compatibility**: Better compatibility with serving frameworks\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import AutoPeftModelForCausalLM\n",
+    "\n",
+    "\n",
+    "# Load PEFT model on CPU\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=args.output_dir,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    low_cpu_mem_usage=True,\n",
+    ")\n",
+    "\n",
+    "# Merge LoRA and base model and save\n",
+    "merged_model = model.merge_and_unload()\n",
+    "merged_model.save_pretrained(\n",
+    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yO6E9quouLl"
+   },
+   "source": [
+    "## 3. Test Model and run Inference\n",
+    "\n",
+    "After the training is done we want to test our model. We will load different samples from the original dataset and evaluate the model on those samples, using a simple loop and accuracy as our metric.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Bonus Exercise: Load LoRA Adapter</h2>\n",
+    "    <p>Use what you learnt from the ecample note book to load your trained LoRA adapter for inference.</p> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "I5B494OdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# free the memory again\n",
+    "del model\n",
+    "del trainer\n",
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "P1UhohVdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from peft import AutoPeftModelForCausalLM\n",
+    "from transformers import AutoTokenizer, pipeline\n",
+    "\n",
+    "# Load Model with PEFT adapter\n",
+    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
+    ")\n",
+    "pipe = pipeline(\n",
+    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "99uFDAuuouLl"
+   },
+   "source": [
+    "Lets test some prompt samples and see how the model performs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "-shSmUbvouLl",
+    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
+   },
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n",
+    "    \"Write a Python function to calculate the factorial of a number.\",\n",
+    "    \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n",
+    "    \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def test_inference(prompt):\n",
+    "    prompt = pipe.tokenizer.apply_chat_template(\n",
+    "        [{\"role\": \"user\", \"content\": prompt}],\n",
+    "        tokenize=False,\n",
+    "        add_generation_prompt=True,\n",
+    "    )\n",
+    "    outputs = pipe(\n",
+    "        prompt,\n",
+    "    )\n",
+    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
+    "\n",
+    "\n",
+    "for prompt in prompts:\n",
+    "    print(f\"    prompt:\\n{prompt}\")\n",
+    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
+    "    print(\"-\" * 50)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
new file mode 100644
index 00000000..b288a4a1
--- /dev/null
+++ b/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
@@ -0,0 +1,5061 @@
+{
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+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "otj46qRbtpnd",
+        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q bitsandbytes datasets accelerate loralib\n",
+        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
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+            "1d3184ff92b7465da9f36eeb5726f021",
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+          ]
+        },
+        "id": "cg3fiQOvmI3Q",
+        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import bitsandbytes as bnb\n",
+        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S65GcxNGA9kz"
+      },
+      "source": [
+        "## Load adapters from the Hub\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517,
+          "referenced_widgets": [
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+          ]
+        },
+        "id": "hsD1VKqeA62Z",
+        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import transformers\n",
+        "from datasets import load_dataset\n",
+        "from peft import LoraConfig, get_peft_model\n",
+        "from peft import PeftModel, PeftConfig\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "\n",
+        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
+        "config = PeftConfig.from_pretrained(peft_model_id)\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    config.base_model_name_or_path,\n",
+        "    return_dict=True,\n",
+        "    load_in_8bit=True,\n",
+        "    device_map=\"auto\",\n",
+        ")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+        "\n",
+        "# Load the Lora model\n",
+        "model = PeftModel.from_pretrained(model, peft_model_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MDqJWba-tpnv",
+        "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
+      },
+      "outputs": [],
+      "source": [
+        "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n",
+        "\n",
+        "with torch.cuda.amp.autocast():\n",
+        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
+        "\n",
+        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
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diff --git a/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md b/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md
new file mode 100644
index 00000000..66926167
--- /dev/null
+++ b/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md
@@ -0,0 +1,74 @@
+# Prompt Tuning
+
+Prompt tuning is a parameter-efficient approach that modifies input representations rather than model weights. Unlike traditional fine-tuning that updates all model parameters, prompt tuning adds and optimizes a small set of trainable tokens while keeping the base model frozen.
+
+## Understanding Prompt Tuning
+
+Prompt tuning is a parameter-efficient alternative to model fine-tuning that prepends trainable continuous vectors (soft prompts) to the input text. Unlike discrete text prompts, these soft prompts are learned through backpropagation while keeping the language model frozen. The method was introduced in ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), which demonstrated that prompt tuning becomes more competitive with model fine-tuning as model size increases. Within the paper, at around 10 billion parameters, prompt tuning matches the performance of model fine-tuning while only modifying a few hundred parameters per task.
+
+These soft prompts are continuous vectors in the model's embedding space that get optimized during training. Unlike traditional discrete prompts that use natural language tokens, soft prompts have no inherent meaning but learn to elicit the desired behavior from the frozen model through gradient descent. The technique is particularly effective for multi-task scenarios since each task requires storing only a small prompt vector (typically a few hundred parameters) rather than a full model copy. This approach not only maintains a minimal memory footprint but also enables rapid task switching by simply swapping prompt vectors without any model reloading.
+
+## Training Process
+
+Soft prompts typically number between 8 and 32 tokens and can be initialized either randomly or from existing text. The initialization method plays a crucial role in the training process, with text-based initialization often performing better than random initialization.
+
+During training, only the prompt parameters are updated while the base model remains frozen. This focused approach uses standard training objectives but requires careful attention to the learning rate and gradient behavior of the prompt tokens.
+
+## Implementation with PEFT
+
+The PEFT library makes implementing prompt tuning straightforward. Here's a basic example:
+
+```python
+from peft import PromptTuningConfig, TaskType, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load base model
+model = AutoModelForCausalLM.from_pretrained("your-base-model")
+tokenizer = AutoTokenizer.from_pretrained("your-base-model")
+
+# Configure prompt tuning
+peft_config = PromptTuningConfig(
+    task_type=TaskType.CAUSAL_LM,
+    num_virtual_tokens=8,  # Number of trainable tokens
+    prompt_tuning_init="TEXT",  # Initialize from text
+    prompt_tuning_init_text="Classify if this text is positive or negative:",
+    tokenizer_name_or_path="your-base-model",
+)
+
+# Create prompt-tunable model
+model = get_peft_model(model, peft_config)
+```
+
+## Comparison to Other Methods
+
+When compared to other PEFT approaches, prompt tuning stands out for its efficiency. While LoRA offers low parameter counts and memory usage but requires loading adapters for task switching, prompt tuning achieves even lower resource usage and enables immediate task switching. Full fine-tuning, in contrast, demands significant resources and requires separate model copies for different tasks.
+
+| Method | Parameters | Memory | Task Switching |
+|--------|------------|---------|----------------|
+| Prompt Tuning | Very Low | Minimal | Easy |
+| LoRA | Low | Low | Requires Loading |
+| Full Fine-tuning | High | High | New Model Copy |
+
+When implementing prompt tuning, start with a small number of virtual tokens (8-16) and increase only if the task complexity demands it. Text initialization typically yields better results than random initialization, especially when using task-relevant text. The initialization strategy should reflect the complexity of your target task.
+
+Training requires slightly different considerations than full fine-tuning. Higher learning rates often work well, but careful monitoring of prompt token gradients is essential. Regular validation on diverse examples helps ensure robust performance across different scenarios.
+
+## Application
+
+Prompt tuning excels in several scenarios:
+
+1. Multi-task deployment
+2. Resource-constrained environments
+3. Rapid task adaptation
+4. Privacy-sensitive applications
+
+As models get smaller, prompt tuning becomes less competitive compared to full fine-tuning. For example, on models like SmolLM2 scales prompt tuning is less relevant than full fine-tuning. 
+
+## Next Steps
+
+⏭️ Move on to the [LoRA Adapters Tutorial](./notebooks/finetune_sft_peft.ipynb) to learn how to fine-tune a model with LoRA adapters.
+
+## Resources
+- [PEFT Documentation](https://huggingface.co/docs/peft)
+- [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691)
+- [Hugging Face Cookbook](https://huggingface.co/learn/cookbook/prompt_tuning_peft)

From 997c55f8dd887181753d87a0dc7d248e14831d02 Mon Sep 17 00:00:00 2001
From: ViniFBN <81927867+ViniFBN@users.noreply.github.com>
Date: Wed, 18 Dec 2024 12:33:24 +0100
Subject: [PATCH 02/63] Sending part of translation of module 3

For now, the jupyter notebooks are not translated yet.
---
 .../README.md                                 | 53 ++++++++--------
 .../lora_adapters.md                          | 54 ++++++++--------
 .../prompt_tuning.md                          | 62 +++++++++----------
 3 files changed, 85 insertions(+), 84 deletions(-)

diff --git a/pt-br/3_parameter_efficient_finetuning/README.md b/pt-br/3_parameter_efficient_finetuning/README.md
index cd88ad6f..e46f2a10 100644
--- a/pt-br/3_parameter_efficient_finetuning/README.md
+++ b/pt-br/3_parameter_efficient_finetuning/README.md
@@ -1,39 +1,40 @@
-# Parameter-Efficient Fine-Tuning (PEFT)
+# Parameter-Efficient Fine-Tuning (PEFT) (Ajuste Fino com Eficiência de Parâmetro)
 
-As language models grow larger, traditional fine-tuning becomes increasingly challenging. A full fine-tuning of even a 1.7B parameter model requires substantial GPU memory, makes storing separate model copies expensive, and risks catastrophic forgetting of the model's original capabilities. Parameter-efficient fine-tuning (PEFT) methods address these challenges by modifying only a small subset of model parameters while keeping most of the model frozen.
+À medida que os modelos de linguagem aumentam, o ajuste fino tradicional torna-se cada vez mais desafiador. O ajuste fino completo de um modelo com 1,7 bilhão de parâmetros requer uma quantidade considerável de memória da GPU, torna caro o armazenamento de cópias separadas do modelo e apresenta o risco de um esquecimento catastrófico das capacidades originais do modelo. Os métodos de ajuste fino com eficiência de parâmetros (PEFT) abordam esses desafios modificando apenas um pequeno subconjunto de parâmetros do modelo e mantendo a maior parte do modelo congelada.
 
-Traditional fine-tuning updates all model parameters during training, which becomes impractical for large models. PEFT methods introduce approaches to adapt models using fewer trainable parameters - often less than 1% of the original model size. This dramatic reduction in trainable parameters enables:
+O ajuste fino tradicional atualiza todos os parâmetros do modelo durante o treinamento, o que se torna impraticável para modelos grandes. Os métodos PEFT introduzem abordagens para adaptar modelos usando menos parâmetros treináveis, geralmente menos de 1% do tamanho do modelo original. Essa redução drástica nos parâmetros treináveis permite:
 
-- Fine-tuning on consumer hardware with limited GPU memory
-- Storing multiple task-specific adaptations efficiently
-- Better generalization in low-data scenarios
-- Faster training and iteration cycles
+- Ajuste fino no hardware do consumidor com memória de GPU limitada
+- Armazenamento eficiente de várias adaptações de tarefas específicas
+- Melhor generalização em cenários com poucos dados
+- Ciclos de treinamento e iteração mais rápidos
 
-## Available Methods
+## Métodos Disponíveis
 
-In this module, we will cover two popular PEFT methods:
+Neste módulo, abordaremos dois métodos populares de PEFT:
 
-### 1️⃣ LoRA (Low-Rank Adaptation)
+### 1️⃣ LoRA (Low-Rank Adaptation - Adaptação de Baixa Classificação)
 
-LoRA has emerged as the most widely adopted PEFT method, offering an elegant solution to efficient model adaptation. Instead of modifying the entire model, **LoRA injects trainable matrices into the model's attention layers.** This approach typically reduces trainable parameters by about 90% while maintaining comparable performance to full fine-tuning. We will explore LoRA in the [LoRA (Low-Rank Adaptation)](./lora_adapters.md) section.
+O LoRA surgiu como o método PEFT mais amplamente adotado, oferecendo uma solução elegante para a adaptação eficiente do modelo. Em vez de modificar o modelo inteiro, o **LoRA injeta matrizes treináveis nas camadas de atenção do modelo.**  Essa abordagem normalmente reduz os parâmetros treináveis em cerca de 90%, mantendo um desempenho comparável ao ajuste fino completo. Exploraremos o LoRA na seção [LoRA (Adaptação de Baixa Classificação)](./lora_adapters.md).
  
-### 2️⃣ Prompt Tuning
+### 2️⃣ Ajuste de Prompts
 
-Prompt tuning offers an **even lighter** approach by **adding trainable tokens to the input** rather than modifying model weights. Prompt tuning is less popular than LoRA, but can be a useful technique for quickly adapting a model to new tasks or domains. We will explore prompt tuning in the [Prompt Tuning](./prompt_tuning.md) section.
+O ajuste de prompts oferece uma abordagem **ainda mais leve** ao **adicionar tokens treináveis à entrada** em vez de modificar os pesos do modelo. O ajuste de prompt é menos popular que o LoRA, mas pode ser uma técnica útil para adaptar rapidamente um modelo a novas tarefas ou domínios. Exploraremos o ajuste de prompt na seção [Ajuste de Prompt](./prompt_tuning.md).
 
-## Exercise Notebooks
+## Cadernos de Exercícios
 
-| Title | Description | Exercise | Link | Colab |
+| Título | Descrição | Exercício | Link | Colab |
 |-------|-------------|----------|------|-------|
-| LoRA Fine-tuning | Learn how to fine-tune models using LoRA adapters | 🐢 Train a model using LoRA<br>🐕 Experiment with different rank values<br>🦁 Compare performance with full fine-tuning | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-| Load LoRA Adapters | Learn how to load and use trained LoRA adapters | 🐢 Load pre-trained adapters<br>🐕 Merge adapters with base model<br>🦁 Switch between multiple adapters | [Notebook](./notebooks/load_lora_adapter_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
-<!-- | Prompt Tuning | Learn how to implement prompt tuning | 🐢 Train soft prompts<br>🐕 Compare different initialization strategies<br>🦁 Evaluate on multiple tasks | [Notebook](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
-
-## Resources
-- [PEFT Documentation](https://huggingface.co/docs/peft)
-- [LoRA Paper](https://arxiv.org/abs/2106.09685)
-- [QLoRA Paper](https://arxiv.org/abs/2305.14314)
-- [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691)
-- [Hugging Face PEFT Guide](https://huggingface.co/blog/peft)
-- [How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
+| Ajuste fino do LoRA | Aprenda a fazer o ajuste fino de modelos usando adaptadores do LoRA | 🐢 Treine um modelo usando o LoRA< br>🐕 Experimente com diferentes valores de classificação<br>🦁 Compare o desempenho com o ajuste fino completo | [Exercício](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | |
+| Carregue adaptadores LoRA | Aprenda como carregar e usar adaptadores LoRA treinados | 🐢 Carregar adaptadores pré-treinados< br>🐕 Mesclar adaptadores com o modelo de base<br>🦁 Alternar entre vários adaptadores | [Exercício](./notebooks/load_lora_adapter_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | 
+<!-- | Ajuste de prompts | Aprenda como implementar o ajuste de prompts | 🐢 Treine prompts flexíveis<br>🐕 Compare diferentes estratégias de inicialização< br>🦁 Avalie em várias tarefas | [Exercício](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
+
+## Referências
+
+- [Documentação PEFT](https://huggingface.co/docs/peft)
+- [Artigo sobre LoRA](https://arxiv.org/abs/2106.09685)
+- [Artigo sobre QLoRA](https://arxiv.org/abs/2305.14314)
+- [Artigo sobre Ajuste de Prompts](https://arxiv.org/abs/2104.08691)
+- [Guia PEFT do Hugging Face](https://huggingface.co/blog/peft)
+- [Como ajustar os LLMs em 2024 com o Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
 - [TRL](https://huggingface.co/docs/trl/index)
\ No newline at end of file
diff --git a/pt-br/3_parameter_efficient_finetuning/lora_adapters.md b/pt-br/3_parameter_efficient_finetuning/lora_adapters.md
index bc41b85c..411af9af 100644
--- a/pt-br/3_parameter_efficient_finetuning/lora_adapters.md
+++ b/pt-br/3_parameter_efficient_finetuning/lora_adapters.md
@@ -1,16 +1,16 @@
-# LoRA (Low-Rank Adaptation)
+# LoRA (Low-Rank Adaptation - Adaptação de Baixa Classificação)
 
-LoRA has become the most widely adopted PEFT method. It works by adding small rank decomposition matrices to the attention weights, typically reducing trainable parameters by about 90%. 
+LoRA tornou-se o método PEFT mais amplamente adotado. Ele funciona adicionando pequenas matrizes de decomposição de classificação aos pesos de atenção, reduzindo tipicamente os parâmetros treináveis em cerca de 90%.
 
-## Understanding LoRA
+## Entendendo o LoRA
 
-LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that freezes the pre-trained model weights and injects trainable rank decomposition matrices into the model's layers. Instead of training all model parameters during fine-tuning, LoRA decomposes the weight updates into smaller matrices through low-rank decomposition, significantly reducing the number of trainable parameters while maintaining model performance. For example, when applied to GPT-3 175B, LoRA reduced trainable parameters by 10,000x and GPU memory requirements by 3x compared to full fine-tuning. You can read more about LoRA in the [LoRA paper](https://arxiv.org/pdf/2106.09685).
+LoRA (Adaptação de Baixa Classificação) é uma técnica de ajuste fino eficiente em termos de parâmetros que congela os pesos do modelo pré-treinado e injeta matrizes treináveis de decomposição de classificação nas camadas do modelo. Em vez de treinar todos os parâmetros do modelo durante o ajuste fino, o LoRA decompõe as atualizações de peso em matrizes menores por meio da decomposição de baixa classificação, reduzindo significativamente o número de parâmetros treináveis enquanto mantém o desempenho do modelo. Por exemplo, quando aplicado ao GPT-3 175B, o LoRA reduziu os parâmetros treináveis em 10.000x e os requisitos de memória GPU em 3x em comparação ao ajuste fino completo. Você pode saber mais sobre o LoRA no [Artigo sobre LoRA](https://arxiv.org/pdf/2106.09685).
 
-LoRA works by adding pairs of rank decomposition matrices to transformer layers, typically focusing on attention weights. During inference, these adapter weights can be merged with the base model, resulting in no additional latency overhead. LoRA is particularly useful for adapting large language models to specific tasks or domains while keeping resource requirements manageable.
+O LoRA funciona adicionando pares de matrizes de decomposição de classificação às camadas do transformer, geralmente focando nos pesos de atenção. Durante a inferência, esses pesos adaptadores podem ser mesclados com o modelo base, resultando em nenhuma sobrecarga adicional de latência. LoRA é particularmente útil para adaptar modelos de linguagem de grande porte a tarefas ou domínios específicos, mantendo os requisitos de recursos gerenciáveis.
 
-## Loading LoRA Adapters
+## Carregando Adaptadores LoRA
 
-Adapters can be loaded onto a pretrained model with load_adapter(), which is useful for trying out different adapters whose weights aren’t merged. Set the active adapter weights with the set_adapter() function. To return the base model, you could use unload() to unload all of the LoRA modules. This makes it easy to switch between different task-specific weights.
+Os adaptadores podem ser carregados em um modelo pré-treinado com load_adapter(), o que é útil para experimentar diferentes adaptadores cujos pesos não estão mesclados. Defina os pesos do adaptador ativo com a função set_adapter(). Para retornar ao modelo base, você pode usar unload() para descarregar todos os módulos LoRA. Isso facilita a alternância entre pesos específicos de tarefas.
 
 ```python
 from transformers import AutoModelForCausalLM
@@ -23,27 +23,27 @@ model = PeftModel.from_pretrained(base_model, peft_model_id)
 
 ![lora_load_adapter](./images/lora_adapter.png)
 
-## Merging LoRA Adapters
+## Mesclando Adaptadores LoRA
 
-After training with LoRA, you might want to merge the adapter weights back into the base model for easier deployment. This creates a single model with the combined weights, eliminating the need to load adapters separately during inference.
+Após o treinamento com LoRA, você pode querer mesclar os pesos do adaptador de volta ao modelo base para facilitar a implantação. Isso cria um único modelo com os pesos combinados, eliminando a necessidade de carregar adaptadores separadamente durante a inferência.
 
-The merging process requires attention to memory management and precision. Since you'll need to load both the base model and adapter weights simultaneously, ensure sufficient GPU/CPU memory is available. Using `device_map="auto"` in `transformers` will help with automatic memory management. Maintain consistent precision (e.g., float16) throughout the process, matching the precision used during training and saving the merged model in the same format for deployment. Before deploying, always validate the merged model by comparing its outputs and performance metrics with the adapter-based version.
+processo de mesclagem requer atenção ao gerenciamento de memória e precisão. Como será necessário carregar o modelo base e os pesos do adaptador simultaneamente, garanta memória suficiente na GPU/CPU. Usar `device_map="auto"` no `transformers` ajudará no gerenciamento automático de memória. Mantenha uma precisão consistente (por exemplo, float16) durante todo o processo, correspondendo à precisão usada durante o treinamento e salvando o modelo mesclado no mesmo formato para implantação. Antes de implantar, sempre valide o modelo mesclado comparando suas saídas e métricas de desempenho com a versão baseada em adaptadores.
 
-Adapters are also be convenient for switching between different tasks or domains. You can load the base model and adapter weights separately. This allows for quick switching between different task-specific weights. 
+Os adaptadores também são convenientes para alternar entre diferentes tarefas ou domínios. Você pode carregar o modelo base e os pesos do adaptador separadamente, permitindo alternâncias rápidas entre pesos específicos de tarefas. 
 
-## Implementation Guide
+## Guia de Implementação
 
-The `notebooks/` directory contains practical tutorials and exercises for implementing different PEFT methods. Begin with `load_lora_adapter_example.ipynb` for a basic introduction, then explore `lora_finetuning.ipynb` for a more detailed look at how to fine-tune a model with LoRA and SFT.
+O diretório `notebooks/` contém tutoriais práticos e exercícios para implementar diferentes métodos PEFT. Comece com `load_lora_adapter_example.ipynb` para uma introdução básica e depois veja `lora_finetuning.ipynb` para um estudo mais detalhado sobre como ajustar um modelo com LoRA e SFT.
 
-When implementing PEFT methods, start with small rank values (4-8) for LoRA and monitor training loss. Use validation sets to prevent overfitting and compare results with full fine-tuning baselines when possible. The effectiveness of different methods can vary by task, so experimentation is key.
+Ao implementar métodos PEFT, comece com valores baixos de classificação (4-8) para LoRA e monitore a perda de treinamento. Use conjuntos de validação para evitar overfitting e compare os resultados com as linhas de base de ajuste fino completo sempre que possível. A eficácia de diferentes métodos pode variar conforme a tarefa, então a experimentação é essencial.
 
 ## OLoRA
 
-[OLoRA](https://arxiv.org/abs/2406.01775) utilizes QR decomposition to initialize the LoRA adapters. OLoRA translates the base weights of the model by a factor of their QR decompositions, i.e., it mutates the weights before performing any training on them. This approach significantly improves stability, accelerates convergence speed, and ultimately achieves superior performance.
+[OLoRA](https://arxiv.org/abs/2406.01775) utiliza decomposição QR para inicializar os adaptadores LoRA. OLoRA traduz os pesos base do modelo por um fator de suas decomposições QR, ou seja, altera os pesos antes de realizar qualquer treinamento sobre eles. Essa abordagem melhora significativamente a estabilidade, acelera a velocidade de convergência e, por fim, alcança um desempenho superior.
 
-## Using TRL with PEFT
+## Usando TRL com PEFT
 
-PEFT methods can be combined with TRL (Transformers Reinforcement Learning) for efficient fine-tuning. This integration is particularly useful for RLHF (Reinforcement Learning from Human Feedback) as it reduces memory requirements.
+Os métodos PEFT podem ser combinados com TRL (Reinforcement Learning com Transformers) para ajuste fino eficiente. Essa integração é particularmente útil para RLHF (Reinforcement Learning from Human Feedback), pois reduz os requisitos de memória.
 
 ```python
 from peft import LoraConfig
@@ -67,11 +67,11 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 ```
 
-Above, we used `device_map="auto"` to automatically assign the model to the correct device. You can also manually assign the model to a specific device using `device_map={"": device_index}`. You could also scale training across multiple GPUs while keeping memory usage efficient.
+No exemplo acima, usamos `device_map="auto"` para atribuir automaticamente o modelo ao dispositivo correto. Você também pode atribuir manualmente o modelo a um dispositivo específico usando `device_map={"": device_index}`. Também é possível escalar o treinamento em várias GPUs enquanto mantém o uso de memória eficiente.
 
-## Basic Merging Implementation
+## Implementação Básica de Mesclagem
 
-After training a LoRA adapter, you can merge the adapter weights back into the base model. Here's how to do it:
+Após treinar um adaptador LoRA, você pode mesclar os pesos do adaptador de volta ao modelo base. Veja como fazer isso:
 
 ```python
 import torch
@@ -103,7 +103,7 @@ except RuntimeError as e:
 merged_model.save_pretrained("path/to/save/merged_model")
 ```
 
-If you encounter size discrepancies in the saved model, ensure you're also saving the tokenizer:
+Se você encontrar discrepâncias de tamanho no modelo salvo, garanta que está salvando também o tokenizador:
 
 ```python
 # Save both model and tokenizer
@@ -112,13 +112,13 @@ merged_model.save_pretrained("path/to/save/merged_model")
 tokenizer.save_pretrained("path/to/save/merged_model")
 ```
 
-## Next Steps
+## Próximos Passos
 
-⏩ Move on to the [Prompt Tuning](prompt_tuning.md) guide to learn how to fine-tune a model with prompt tuning.
-⏩ Move on the [Load LoRA Adapters Tutorial](./notebooks/load_lora_adapter.ipynb) to learn how to load LoRA adapters.
+⏩ Prossiga para o guia de [Ajuste de Prompts](prompt_tuning.md) para aprender como ajustar um modelo com ajuste de prompts.
+⏩ Veja o [Tutorial de Carregamento de Adaptadores LoRA](./notebooks/load_lora_adapter.ipynb) para aprender como carregar adaptadores LoRA.
 
-# Resources
+# Referências
 
 - [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
-- [PEFT Documentation](https://huggingface.co/docs/peft)
-- [Hugging Face blog post on PEFT](https://huggingface.co/blog/peft)
+- [Documentação PEFT](https://huggingface.co/docs/peft)
+- [Blog do Hugging Face sobre PEFT](https://huggingface.co/blog/peft)
diff --git a/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md b/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md
index 66926167..7e48dc42 100644
--- a/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md
+++ b/pt-br/3_parameter_efficient_finetuning/prompt_tuning.md
@@ -1,22 +1,22 @@
-# Prompt Tuning
+# Ajuste de Prompts
 
-Prompt tuning is a parameter-efficient approach that modifies input representations rather than model weights. Unlike traditional fine-tuning that updates all model parameters, prompt tuning adds and optimizes a small set of trainable tokens while keeping the base model frozen.
+O ajuste de prompts (prompt tuning) é uma abordagem eficiente em termos de parâmetros que modifica as representações de entrada em vez dos pesos do modelo. Diferente do ajuste fino tradicional, que atualiza todos os parâmetros do modelo, o ajuste de prompts adiciona e otimiza um pequeno conjunto de tokens treináveis, mantendo o modelo base congelado.
 
-## Understanding Prompt Tuning
+## Entendendo o Ajuste de Prompts
 
-Prompt tuning is a parameter-efficient alternative to model fine-tuning that prepends trainable continuous vectors (soft prompts) to the input text. Unlike discrete text prompts, these soft prompts are learned through backpropagation while keeping the language model frozen. The method was introduced in ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), which demonstrated that prompt tuning becomes more competitive with model fine-tuning as model size increases. Within the paper, at around 10 billion parameters, prompt tuning matches the performance of model fine-tuning while only modifying a few hundred parameters per task.
+O ajuste de prompts é uma alternativa eficiente ao ajuste fino de modelos que adiciona vetores contínuos treináveis (soft prompts) ao texto de entrada. Diferente dos prompts de texto discretos, esses soft prompts são aprendidos através de retropropagação enquanto o modelo de linguagem permanece congelado. O método foi introduzido em ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), que demonstrou que o ajuste de prompts se torna mais competitivo em relação ao ajuste fino conforme o tamanho do modelo aumenta. No artigo, em torno de 10 bilhões de parâmetros, o ajuste de prompts iguala o desempenho do ajuste fino, modificando apenas algumas centenas de parâmetros por tarefa.
 
-These soft prompts are continuous vectors in the model's embedding space that get optimized during training. Unlike traditional discrete prompts that use natural language tokens, soft prompts have no inherent meaning but learn to elicit the desired behavior from the frozen model through gradient descent. The technique is particularly effective for multi-task scenarios since each task requires storing only a small prompt vector (typically a few hundred parameters) rather than a full model copy. This approach not only maintains a minimal memory footprint but also enables rapid task switching by simply swapping prompt vectors without any model reloading.
+Esses soft prompts são vetores contínuos no espaço de embedding do modelo que são otimizados durante o treinamento. Diferente dos prompts discretos tradicionais que usam tokens de linguagem natural, os soft prompts não possuem significado inerente, mas aprendem a evocar o comportamento desejado do modelo congelado por meio de gradiente descendente. A técnica é particularmente eficaz em cenários multitarefa, pois cada tarefa exige apenas o armazenamento de um pequeno vetor de prompt (normalmente algumas centenas de parâmetros) em vez de uma cópia completa do modelo. Essa abordagem não apenas mantém um uso mínimo de memória, mas também possibilita a troca rápida de tarefas apenas trocando os vetores de prompt sem precisar recarregar o modelo.
 
-## Training Process
+## Processo de Treinamento
 
-Soft prompts typically number between 8 and 32 tokens and can be initialized either randomly or from existing text. The initialization method plays a crucial role in the training process, with text-based initialization often performing better than random initialization.
+Os soft prompts geralmente têm entre 8 e 32 tokens e podem ser inicializados aleatoriamente ou a partir de texto existente. O método de inicialização desempenha um papel crucial no processo de treinamento, com inicializações baseadas em texto frequentemente apresentando melhor desempenho do que inicializações aleatórias.
 
-During training, only the prompt parameters are updated while the base model remains frozen. This focused approach uses standard training objectives but requires careful attention to the learning rate and gradient behavior of the prompt tokens.
+Durante o treinamento, apenas os parâmetros do prompt são atualizados, enquanto o modelo base permanece congelado. Essa abordagem focada utiliza objetivos de treinamento padrão, mas exige atenção cuidadosa à taxa de aprendizado e ao comportamento do gradiente dos tokens do prompt.
 
-## Implementation with PEFT
+## Implementação com PEFT
 
-The PEFT library makes implementing prompt tuning straightforward. Here's a basic example:
+O módulo PEFT facilita a implementação do ajuste de prompts. Aqui está um exemplo básico:
 
 ```python
 from peft import PromptTuningConfig, TaskType, get_peft_model
@@ -39,36 +39,36 @@ peft_config = PromptTuningConfig(
 model = get_peft_model(model, peft_config)
 ```
 
-## Comparison to Other Methods
+## Comparação com Outros Métodos
 
-When compared to other PEFT approaches, prompt tuning stands out for its efficiency. While LoRA offers low parameter counts and memory usage but requires loading adapters for task switching, prompt tuning achieves even lower resource usage and enables immediate task switching. Full fine-tuning, in contrast, demands significant resources and requires separate model copies for different tasks.
+Quando comparado a outras abordagens PEFT, o ajuste de prompts se destaca por sua eficiência. Enquanto o LoRA oferece baixo uso de parâmetros e memória, mas exige o carregamento de adaptadores para troca de tarefas, o ajuste de prompts atinge um uso de recursos ainda menor e possibilita a troca imediata de tarefas. O ajuste fino completo, em contraste, demanda recursos significativos e requer cópias separadas do modelo para diferentes tarefas.
 
-| Method | Parameters | Memory | Task Switching |
+| Método | Parâmetros | Memória | Troca de Tarefas |
 |--------|------------|---------|----------------|
-| Prompt Tuning | Very Low | Minimal | Easy |
-| LoRA | Low | Low | Requires Loading |
-| Full Fine-tuning | High | High | New Model Copy |
+| Ajuste de Prompts| Muito Baixo | Mínima | Fácil |
+| LoRA | Baixo | Baixa | Requer Carregamento |
+| Ajuste Fino | Alto | Alta | Nova Cópia do Modelo |
 
-When implementing prompt tuning, start with a small number of virtual tokens (8-16) and increase only if the task complexity demands it. Text initialization typically yields better results than random initialization, especially when using task-relevant text. The initialization strategy should reflect the complexity of your target task.
+Ao implementar o ajuste de prompts, comece com um pequeno número de tokens virtuais (8-16) e aumente apenas se a complexidade da tarefa exigir. A inicialização baseada em texto geralmente apresenta melhores resultados do que a inicialização aleatória, especialmente ao usar texto relevante para a tarefa. A estratégia de inicialização deve refletir a complexidade da tarefa alvo.
 
-Training requires slightly different considerations than full fine-tuning. Higher learning rates often work well, but careful monitoring of prompt token gradients is essential. Regular validation on diverse examples helps ensure robust performance across different scenarios.
+O treinamento requer considerações ligeiramente diferentes do ajuste fino completo. Taxas de aprendizado mais altas geralmente funcionam bem, mas o monitoramento cuidadoso dos gradientes dos tokens do prompt é essencial. A validação regular com exemplos diversos ajuda a garantir um desempenho robusto em diferentes cenários.
 
-## Application
+## Aplicação
 
-Prompt tuning excels in several scenarios:
+O ajuste de prompts se destaca em diversos cenários:
 
-1. Multi-task deployment
-2. Resource-constrained environments
-3. Rapid task adaptation
-4. Privacy-sensitive applications
+1. Implantação multitarefa
+2. Ambientes com restrição de recursos
+3. Adaptação rápida a tarefas
+4. Aplicações sensíveis à privacidade
 
-As models get smaller, prompt tuning becomes less competitive compared to full fine-tuning. For example, on models like SmolLM2 scales prompt tuning is less relevant than full fine-tuning. 
+Conforme os modelos ficam menores, o ajuste de prompts se torna menos competitivo em comparação ao ajuste fino completo. Por exemplo, em modelos como SmolLM2, o ajuste de prompts é menos relevante do que o ajuste fino completo. 
 
-## Next Steps
+## Próximos Passos
 
-⏭️ Move on to the [LoRA Adapters Tutorial](./notebooks/finetune_sft_peft.ipynb) to learn how to fine-tune a model with LoRA adapters.
+⏭️ Prossiga para o [Tutorial de Adaptadores LoRA](./notebooks/finetune_sft_peft.ipynb) para aprender como ajustar um modelo com adaptadores LoRA.
 
-## Resources
-- [PEFT Documentation](https://huggingface.co/docs/peft)
-- [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691)
-- [Hugging Face Cookbook](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
+## Referências
+- [Documentação PEFT](https://huggingface.co/docs/peft)
+- [Artigo sobre Ajuste de Prompts](https://arxiv.org/abs/2104.08691)
+- [Cookbook do Hugging Face](https://huggingface.co/learn/cookbook/prompt_tuning_peft)

From d6249577dc2e99ff76153c136e74f7f378067f50 Mon Sep 17 00:00:00 2001
From: ViniFBN <81927867+ViniFBN@users.noreply.github.com>
Date: Wed, 18 Dec 2024 13:52:39 +0100
Subject: [PATCH 03/63] Finished translation - Module 3

Module 3 fully translated to brazilian portuguese and up-to-date with the main commits of the original repo
---
 .../README.md                                 |   2 +-
 .../images/lora_adapter.png                   | Bin 1239572 -> 849480 bytes
 .../notebooks/finetune_sft_peft.ipynb         | 111 +++++++++---------
 .../notebooks/load_lora_adapter.ipynb         |   2 +-
 4 files changed, 57 insertions(+), 58 deletions(-)

diff --git a/pt-br/3_parameter_efficient_finetuning/README.md b/pt-br/3_parameter_efficient_finetuning/README.md
index e46f2a10..1d015514 100644
--- a/pt-br/3_parameter_efficient_finetuning/README.md
+++ b/pt-br/3_parameter_efficient_finetuning/README.md
@@ -26,7 +26,7 @@ O ajuste de prompts oferece uma abordagem **ainda mais leve** ao **adicionar tok
 | Título | Descrição | Exercício | Link | Colab |
 |-------|-------------|----------|------|-------|
 | Ajuste fino do LoRA | Aprenda a fazer o ajuste fino de modelos usando adaptadores do LoRA | 🐢 Treine um modelo usando o LoRA< br>🐕 Experimente com diferentes valores de classificação<br>🦁 Compare o desempenho com o ajuste fino completo | [Exercício](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | |
-| Carregue adaptadores LoRA | Aprenda como carregar e usar adaptadores LoRA treinados | 🐢 Carregar adaptadores pré-treinados< br>🐕 Mesclar adaptadores com o modelo de base<br>🦁 Alternar entre vários adaptadores | [Exercício](./notebooks/load_lora_adapter_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | 
+| Carregue adaptadores LoRA | Aprenda como carregar e usar adaptadores LoRA treinados | 🐢 Carregar adaptadores pré-treinados< br>🐕 Mesclar adaptadores com o modelo de base<br>🦁 Alternar entre vários adaptadores | [Exercício](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | 
 <!-- | Ajuste de prompts | Aprenda como implementar o ajuste de prompts | 🐢 Treine prompts flexíveis<br>🐕 Compare diferentes estratégias de inicialização< br>🦁 Avalie em várias tarefas | [Exercício](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
 
 ## Referências
diff --git a/pt-br/3_parameter_efficient_finetuning/images/lora_adapter.png b/pt-br/3_parameter_efficient_finetuning/images/lora_adapter.png
index 65aba13fc2656cf0b63c685166c1d401a6285bea..10e9a36c70501420f378c18a54cc78952e0a7c20 100644
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diff --git a/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
index 3d68083a..472a2c34 100644
--- a/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ b/pt-br/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -6,19 +6,19 @@
     "id": "z-6LLOPZouLg"
    },
    "source": [
-    "# How to Fine-Tune LLMs with LoRA Adapters using Hugging Face TRL\n",
-    "\n",
-    "This notebook demonstrates how to efficiently fine-tune large language models using LoRA (Low-Rank Adaptation) adapters. LoRA is a parameter-efficient fine-tuning technique that:\n",
-    "- Freezes the pre-trained model weights\n",
-    "- Adds small trainable rank decomposition matrices to attention layers\n",
-    "- Typically reduces trainable parameters by ~90%\n",
-    "- Maintains model performance while being memory efficient\n",
-    "\n",
-    "We'll cover:\n",
-    "1. Setup development environment and LoRA configuration\n",
-    "2. Create and prepare the dataset for adapter training\n",
-    "3. Fine-tune using `trl` and `SFTTrainer` with LoRA adapters\n",
-    "4. Test the model and merge adapters (optional)\n"
+    "# Como Ajustar LLMs com Adaptadores LoRA usando Hugging Face TRL\n",
+    "\n",
+    "Este notebook demonstra como ajustar eficientemente grandes modelos de linguagem usando adaptadores LoRA (Adaptação de Baixa Classificação). LoRA é uma técnica de ajuste fino eficiente em termos de parâmetros que:\n",
+    "- Congela os pesos do modelo pré-treinado\n",
+    "- Adiciona pequenas matrizes treináveis de decomposição de classificação às camadas de atenção\n",
+    "- Normalmente reduz os parâmetros treináveis em ~90%\n",
+    "- Mantém o desempenho do modelo sendo eficiente em termos de memória\n",
+    "\n",
+    "Vamos abordar:\n",
+    "1. Configurar o ambiente de desenvolvimento e a configuração do LoRA\n",
+    "2. Criar e preparar o conjunto de dados para o treinamento do adaptador\n",
+    "3. Ajustar o modelo usando `trl` e `SFTTrainer` com adaptadores LoRA\n",
+    "4. Testar o modelo e mesclar adaptadores (opcional)\n"
    ]
   },
   {
@@ -27,9 +27,9 @@
     "id": "fXqd9BXgouLi"
    },
    "source": [
-    "## 1. Setup development environment\n",
+    "## 1. Configurar o ambiente de desenvolvimento\n",
     "\n",
-    "Our first step is to install Hugging Face Libraries and Pyroch, including trl, transformers and datasets. If you haven't heard of trl yet, don't worry. It is a new library on top of transformers and datasets, which makes it easier to fine-tune, rlhf, align open LLMs.\n"
+    "Nosso primeiro passo é instalar os módulos do Hugging Face e o PyTorch, incluindo trl, transformers e datasets. Se você ainda não ouviu falar do trl, não se preocupe. É uma nova biblioteca baseada em transformers e datasets, que facilita o ajuste fino, o RLHF (Reinforcement Learning from Human Feedback) e o alinhamento de modelos de linguagem abertos (open LLMs).\n"
    ]
   },
   {
@@ -58,7 +58,7 @@
     "id": "XHUzfwpKouLk"
    },
    "source": [
-    "## 2. Load the dataset"
+    "## 2. Carregue o conjunto de dados"
    ]
   },
   {
@@ -103,27 +103,28 @@
     "id": "9TOhJdtsouLk"
    },
    "source": [
-    "## 3. Fine-tune LLM using `trl` and the `SFTTrainer` with LoRA\n",
+    "## 3. Ajustar uma LLM usando `trl` e o `SFTTrainer` com o LoRA\n",
     "\n",
-    "The [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) from `trl` provides integration with LoRA adapters through the [PEFT](https://huggingface.co/docs/peft/en/index) library. Key advantages of this setup include:\n",
+    "O [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) do `trl` oferece integração com adaptadores LoRA através do módulo [PEFT](https://huggingface.co/docs/peft/en/index). As principais vantagens dessa configuração incluem:\n",
+    "\n",
+    "1. **Eficiência de Memória**: \n",
+    "   - Apenas os parâmetros dos adaptadores são armazenados na memória da GPU\n",
+    "   - Os pesos do modelo base permanecem congelados e podem ser carregados em precisão reduzida\n",
     "\n",
-    "1. **Memory Efficiency**: \n",
-    "   - Only adapter parameters are stored in GPU memory\n",
-    "   - Base model weights remain frozen and can be loaded in lower precision\n",
     "   - Enables fine-tuning of large models on consumer GPUs\n",
     "\n",
-    "2. **Training Features**:\n",
-    "   - Native PEFT/LoRA integration with minimal setup\n",
-    "   - Support for QLoRA (Quantized LoRA) for even better memory efficiency\n",
+    "2. **Recursos de Treinamento**:\n",
+    "   - Integração nativa PEFT/LoRA com configuração mínima\n",
+    "   - Suporte ao QLoRA (LoRA Quantizado) para uma eficiência de memória ainda melhor\n",
     "\n",
-    "3. **Adapter Management**:\n",
-    "   - Adapter weight saving during checkpoints\n",
-    "   - Features to merge adapters back into base model\n",
+    "3. **Gerenciamento de Adaptadores**:\n",
+    "   - Salvamento de pesos dos adaptadores durante checkpoints\n",
+    "   - Recursos para mesclar adaptadores de volta ao modelo base\n",
     "\n",
-    "We'll use LoRA in our example, which combines LoRA with 4-bit quantization to further reduce memory usage without sacrificing performance. The setup requires just a few configuration steps:\n",
-    "1. Define the LoRA configuration (rank, alpha, dropout)\n",
-    "2. Create the SFTTrainer with PEFT config\n",
-    "3. Train and save the adapter weights\n"
+    "Usaremos LoRA em nosso exemplo, que combina LoRA com quantização de 4 bits para reduzir ainda mais o uso de memória sem sacrificar o desempenho. A configuração requer apenas algumas etapas:\n",
+    "1. Definir a configuração do LoRA (rank, alpha, dropout)\n",
+    "2. Criar o SFTTrainer com configuração PEFT\n",
+    "3. Treinar e salvar os pesos dos adaptadores\n"
    ]
   },
   {
@@ -166,15 +167,15 @@
     "id": "ZbuVArTHouLk"
    },
    "source": [
-    "The `SFTTrainer`  supports a native integration with `peft`, which makes it super easy to efficiently tune LLMs using, e.g. LoRA. We only need to create our `LoraConfig` and provide it to the trainer.\n",
-    "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Define LoRA parameters for finetuning</h2>\n",
-    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Use the general parameters for an abitrary finetune</p>\n",
-    "    <p>🐕 Adjust the parameters and review in weights & biases.</p>\n",
-    "    <p>🦁 Adjust the parameters and show change in inference results.</p>\n",
+    "O `SFTTrainer` oferece uma integração nativa com o `peft`, tornando extremamente fácil ajustar eficientemente LLMs, como com o LoRA, por exemplo. Precisamos apenas criar nosso `LoraConfig` e fornecê-lo ao trainer.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'> \n",
+    "    <h2 style='margin: 0;color:blue'>Exercício: Definir parâmetros LoRA para ajuste fino</h2> \n",
+    "    <p>Escolha um conjunto de dados do hub do Hugging Face e ajuste um modelo com ele.</p> \n",
+    "    <p><b>Níveis de Dificuldade</b></p> \n",
+    "    <p>🐢 Use os parâmetros gerais para um ajuste fino arbitrário.</p> \n",
+    "    <p>🐕 Ajuste os parâmetros e revise em pesos & biases.</p> \n",
+    "    <p>🦁 Ajuste os parâmetros e mostre a mudança nos resultados de inferência.</p> \n",
     "</div>"
    ]
   },
@@ -212,7 +213,7 @@
     "id": "l5NUDPcaouLl"
    },
    "source": [
-    "Before we can start our training we need to define the hyperparameters (`TrainingArguments`) we want to use."
+    "Antes de iniciarmos nosso treinamento, precisamos definir os hiperparâmetros (`TrainingArguments`) que queremos usar.\n"
    ]
   },
   {
@@ -259,7 +260,7 @@
     "id": "cGhR7uFBouLl"
    },
    "source": [
-    "We now have every building block we need to create our `SFTTrainer` to start then training our model."
+    "Agora temos todos os pedaços que precisamos para criar nosso `SFTTrainer` para então começar a treinar nosso modelo."
    ]
   },
   {
@@ -294,7 +295,7 @@
     "id": "zQ_kRN24ouLl"
    },
    "source": [
-    "Start training our model by calling the `train()` method on our `Trainer` instance. This will start the training loop and train our model for 3 epochs. Since we are using a PEFT method, we will only save the adapted model weights and not the full model."
+    "Comece treinando nosso modelo chamando a função `train ()` em nossa instância de `Trainer`. Isso iniciará o loop de treinamento e treinará nosso modelo por 3 épocas. Como estamos usando um método PEFT, salvaremos apenas os pesos do modelo adaptado e não o do modelo completo."
    ]
   },
   {
@@ -343,8 +344,7 @@
     "id": "y4HHSYYzouLl"
    },
    "source": [
-    "The training with Flash Attention for 3 epochs with a dataset of 15k samples took 4:14:36 on a `g5.2xlarge`. The instance costs `1.21$/h` which brings us to a total cost of only ~`5.3$`.\n",
-    "\n"
+    "O treinamento com Flash Attention por 3 épocas com um conjunto de dados de 15 mil amostras levou 4:14:36 ​​em um `g5.2xlarge`. A instância custa `1,21 dólares/h`, o que nos leva a um custo total de apenas ~`5,3 dólares`.\n"
    ]
   },
   {
@@ -353,13 +353,13 @@
     "id": "C309KsXjouLl"
    },
    "source": [
-    "### Merge LoRA Adapter into the Original Model\n",
+    "### Mesclar Adaptador LoRA no Modelo Original\n",
     "\n",
-    "When using LoRA, we only train adapter weights while keeping the base model frozen. During training, we save only these lightweight adapter weights (~2-10MB) rather than a full model copy. However, for deployment, you might want to merge the adapters back into the base model for:\n",
+    "Ao usar LoRA, treinamos apenas os pesos do adaptador mantendo o modelo base congelado. Durante o treinamento, salvamos apenas esses pesos de adaptador leves (~2-10MB) em vez de uma cópia completa do modelo. No entanto, para implantação, você talvez queira mesclar os adaptadores de volta ao modelo base para:\n",
     "\n",
-    "1. **Simplified Deployment**: Single model file instead of base model + adapters\n",
-    "2. **Inference Speed**: No adapter computation overhead\n",
-    "3. **Framework Compatibility**: Better compatibility with serving frameworks\n"
+    "1. **Implantação Simplificada**: Arquivo de modelo único em vez de modelo base + adaptadores\n",
+    "2. **Velocidade de Inferência**: Sem sobrecarga de computação de adaptadores\n",
+    "3. **Compatibilidade de Framework**: Melhor compatibilidade com serving frameworks \n"
    ]
   },
   {
@@ -391,10 +391,9 @@
     "id": "-yO6E9quouLl"
    },
    "source": [
-    "## 3. Test Model and run Inference\n",
+    "## 3. Testar Modelo e Executar Inferência\n",
     "\n",
-    "After the training is done we want to test our model. We will load different samples from the original dataset and evaluate the model on those samples, using a simple loop and accuracy as our metric.\n",
-    "\n"
+    "Após a conclusão do treinamento, queremos testar nosso modelo. Vamos carregar diferentes amostras do conjunto de dados original e avaliar o modelo baseado nessas amostras, usando um loop simples e a precisão como nossa métrica."
    ]
   },
   {
@@ -402,8 +401,8 @@
    "metadata": {},
    "source": [
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bonus Exercise: Load LoRA Adapter</h2>\n",
-    "    <p>Use what you learnt from the ecample note book to load your trained LoRA adapter for inference.</p> \n",
+    "    <h2 style='margin: 0;color:blue'>Exercício Bônus: Carregar Adaptador LoRA</h2>\n",
+    "    <p>Use o que você aprendeu no caderno de exemplo para carregar seu adaptador LoRA treinado para inferência.</p> \n",
     "</div>"
    ]
   },
@@ -449,7 +448,7 @@
     "id": "99uFDAuuouLl"
    },
    "source": [
-    "Lets test some prompt samples and see how the model performs."
+    "Vamos testar uns exemplos de prompts e ver como o modelo reage."
    ]
   },
   {
diff --git a/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
index b288a4a1..0a41f846 100644
--- a/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
+++ b/pt-br/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
@@ -156,7 +156,7 @@
         "id": "S65GcxNGA9kz"
       },
       "source": [
-        "## Load adapters from the Hub\n",
+        "## Carregue os adaptadores do Hub\n",
         "\n"
       ]
     },

From 811f6fe79c4bcf75745d876de13c24c57764c5d2 Mon Sep 17 00:00:00 2001
From: yabramuvdi <yabran.muvdi@gmail.com>
Date: Wed, 18 Dec 2024 20:51:50 +0100
Subject: [PATCH 04/63] first version of Spanish translation

---
 es/4_evaluation/README.md                     |    36 +
 es/4_evaluation/automatic_benchmarks.md       |   136 +
 es/4_evaluation/custom_evaluation.md          |   132 +
 ...hteval_evaluate_and_analyse_your_LLM.ipynb | 19409 ++++++++++++++++
 es/4_evaluation/project/README.md             |    83 +
 es/4_evaluation/project/annotate_dataset.py   |   129 +
 es/4_evaluation/project/create_dataset.py     |    72 +
 es/4_evaluation/project/evaluation_task.py    |    86 +
 es/4_evaluation/project/generate_dataset.py   |   167 +
 .../images/domain_eval_argilla_view.png       |   Bin 0 -> 197693 bytes
 .../images/domain_eval_dataset_viewer.png     |   Bin 0 -> 89114 bytes
 11 files changed, 20250 insertions(+)
 create mode 100644 es/4_evaluation/README.md
 create mode 100644 es/4_evaluation/automatic_benchmarks.md
 create mode 100644 es/4_evaluation/custom_evaluation.md
 create mode 100644 es/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
 create mode 100644 es/4_evaluation/project/README.md
 create mode 100644 es/4_evaluation/project/annotate_dataset.py
 create mode 100644 es/4_evaluation/project/create_dataset.py
 create mode 100644 es/4_evaluation/project/evaluation_task.py
 create mode 100644 es/4_evaluation/project/generate_dataset.py
 create mode 100644 es/4_evaluation/project/images/domain_eval_argilla_view.png
 create mode 100644 es/4_evaluation/project/images/domain_eval_dataset_viewer.png

diff --git a/es/4_evaluation/README.md b/es/4_evaluation/README.md
new file mode 100644
index 00000000..37e2c58c
--- /dev/null
+++ b/es/4_evaluation/README.md
@@ -0,0 +1,36 @@
+# Evaluación
+
+La evaluación es un paso crítico en el desarrollo y despliegue de modelos de lenguaje. Nos permite entender qué tan bien funcionan nuestros modelos en diferentes capacidades e identificar áreas de mejora. Este módulo cubre tanto los "becnhmarks" estándares como los enfoques de evaluación específicos para evaluar de manera integral tu modelo **smol**.
+
+Usaremos [`lighteval`](https://github.com/huggingface/lighteval), una poderosa biblioteca de evaluación desarrollada por Hugging Face que se integra perfectamente con el ecosistema de Hugging Face. Para una explicación más detallada sobre los conceptos y mejores prácticas de evaluación, consulta la [guía de evaluación](https://github.com/huggingface/evaluation-guidebook).
+
+## Descripción del Módulo
+
+Una estrategia de evaluación completa examina múltiples aspectos del rendimiento del modelo. Evaluamos capacidades específicas en tareas como responder preguntas y resumir textos, para entender cómo el modelo maneja diferentes tipos de problemas. Medimos la calidad del "output" mediante factores como coherencia y precisión. A su vez, la evaluación de seguridad ayuda a identificar posibles "outputs" dañinas o sesgos. Finalmente, las pruebas de experticia en áreas especfícios verifican el conocimiento especializado del modelo en tu campo objetivo.
+
+## Contenidos
+
+### 1️⃣ [Evaluaciones Automáticas](./automatic_benchmarks.md)
+Aprende a evaluar tu modelo utilizando "benchmarks" y métricas estandarizadas. Exploraremos "benchmarks" comunes como MMLU y TruthfulQA, entenderemos las métricas clave de evaluación y configuraciones, y cubriremos mejores prácticas para una evaluación reproducible.
+
+### 2️⃣ [Evaluación Personalizada en un Dominio](./custom_evaluation.md)
+Descubre cómo crear flujos de evaluación adaptados a tus casos de uso específicos. Te guiaremos en el diseño de tareas de evaluación personalizadas, la implementación de métricas especializadas y la construcción de conjuntos de datos de evaluación que se ajusten a tus necesidades.
+
+### 3️⃣ [Proyecto de Evaluación en un Dominio](./project/README.md)
+Sigue un ejemplo completo de cómo construir un flujo de evaluación específico para un dominio. Aprenderás a generar conjuntos de datos de evaluación, usar Argilla para la anotación de datos, crear conjuntos de datos estandarizados y evaluar modelos utilizando LightEval.
+
+### Cuadernos de Ejercicios
+
+| Título | Descripción | Ejercicio | Enlace | Colab |
+|--------|-------------|----------|-------|-------|
+| Evalúa y analiza tu LLM | Aprende a usar LightEval para evaluar y comparar modelos en dominios específicos | 🐢 Usa tareas del dominio médico para evaluar un modelo <br> 🐕 Crea una evaluación de dominio con diferentes tareas MMLU <br> 🦁 Diseña una tarea de evaluación personalizada para tu dominio | [Cuaderno](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## Recursos
+
+- [Guía de Evaluación](https://github.com/huggingface/evaluation-guidebook) - Guía completa para la evaluación de modelos de lenguaje
+- [Documentación de LightEval](https://github.com/huggingface/lighteval) - Documentación oficial de la biblioteca LightEval
+- [Documentación de Argilla](https://docs.argilla.io) - Aprende sobre la plataforma de anotación Argilla
+- [Paper de MMLU](https://arxiv.org/abs/2009.03300) - Artículo sobre el benchmark MMLU
+- [Crear una Tarea Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Crear una Métrica Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Usar métricas existentes](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/es/4_evaluation/automatic_benchmarks.md b/es/4_evaluation/automatic_benchmarks.md
new file mode 100644
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+++ b/es/4_evaluation/automatic_benchmarks.md
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+# Benchmarks Automáticos
+
+Los "benchmarks" automáticos sirven como herramientas estandarizadas para evaluar modelos de lenguaje en diferentes tareas y capacidades. Aunque proporcionan un punto de partida útil para entender el rendimiento de un modelo, es importante reconocer que representan solo una parte de una estrategia de evaluación integral.
+
+## Entendiendo los Benchmarks Automáticos
+
+Los "benchmarks" automáticos generalmente consisten en conjuntos de datos curados con tareas y métricas de evaluación predefinidas. Estos "benchmarks" buscan evaluar diversos aspectos de la capacidad del modelo, desde la comprensión básica del lenguaje hasta el razonamiento complejo. La principal ventaja de usar "benchmarks" automáticos es su estandarización: permiten comparaciones consistentes entre diferentes modelos y proporcionan resultados reproducibles.
+
+Sin embargo, es crucial entender que el rendimiento en un "benchmark" no siempre se traduce directamente en efectividad en el mundo real. Un modelo que sobresale en "benchmarks" académicos puede tener dificultades en aplicaciones específicas de dominio o casos de uso prácticos.
+
+## Benchmarks y Sus Limitaciones
+
+### Benchmarks de Conocimientos Generales
+
+MMLU (Massive Multitask Language Understanding) evalúa conocimientos en 57 materias, desde ciencias hasta humanidades. Aunque es extenso, puede no reflejar la profundidad de experiencia necesaria para dominios específicos. TruthfulQA evalúa la tendencia de un modelo a reproducir conceptos erróneos comunes, aunque no puede capturar todas las formas de desinformación.
+
+### Benchmarks de Razonamiento
+
+BBH (Big Bench Hard) y GSM8K se enfocan en tareas de razonamiento complejo. BBH evalúa el pensamiento lógico y la planificación, mientras que GSM8K se centra específicamente en la resolución de problemas matemáticos. Estos "benchmarks" ayudan a evaluar capacidades analíticas pero pueden no capturar el razonamiento matizado requerido en escenarios del mundo real.
+
+### Comprensión del Lenguaje
+
+HELM proporciona un marco de evaluación holístico, mientras que WinoGrande prueba el sentido común mediante la desambiguación de pronombres. Estos "benchmarks" ofrecen información sobre las capacidades de procesamiento del lenguaje, pero pueden no representar completamente la complejidad de las conversaciones naturales o la terminología específica de un dominio.
+
+## Enfoques Alternativos de Evaluación
+
+Muchas organizaciones han desarrollado métodos de evaluación alternativos para abordar las limitaciones de los "benchmarks" estándar:
+
+### Model de lenguaje como Juez (LLM-as-Judge)
+
+Usar un modelo de lenguaje para evaluar los "outputs" de otro modelo se ha vuelto cada vez más popular. Este enfoque puede proporcionar retroalimentación más matizada que las métricas tradicionales, aunque viene con sus propios sesgos y limitaciones.
+
+### Arenas de Evaluación (Evaluation Arenas)
+
+Plataformas como Chatbot Arena permiten que los modelos interactúen y se evalúen entre sí en entornos controlados. Esto puede revelar fortalezas y debilidades que podrían no ser evidentes en "benchmarks" tradicionales.
+
+### Grupos de Benchmarks Personalizados
+
+Las organizaciones a menudo desarrollan conjuntos de "benchmarks" internos adaptados a sus necesidades específicas y casos de uso. Estos pueden incluir pruebas de conocimientos específicos de dominio o escenarios de evaluación que reflejen las condiciones reales de despliegue.
+
+## Creando Tu Propia Estrategia de Evaluación
+
+Recuerda que aunque LightEval facilita ejecutar "benchmarks" estándar, también deberías invertir tiempo en desarrollar métodos de evaluación específicos para tu caso de uso.
+
+Aunque los "benchmarks" estándares proporcionan una línea base útil, no deberían ser tu único método de evaluación. Así es como puedes desarrollar un enfoque más integral:
+
+1. Comienza con "benchmarks" estándares relevantes para establecer una línea base y permitir comparaciones con otros modelos.
+
+2. Identifica los requisitos y desafíos específicos de tu caso de uso. ¿Qué tareas realizará tu modelo realmente? ¿Qué tipos de errores serían más problemáticos?
+
+3. Desarrolla conjuntos de datos de evaluación personalizados que reflejen tu caso de uso real. Esto podría incluir:
+   - Consultas reales de usuarios en tu dominio
+   - Casos límite comunes que hayas encontrado
+   - Ejemplos de escenarios particularmente desafiantes
+
+4. Considera implementar una estrategia de evaluación por capas:
+   - Métricas automatizadas para retroalimentación rápida
+   - Evaluación humana para una comprensión más matizada
+   - Revisión por expertos en el dominio para aplicaciones especializadas
+   - Pruebas A/B en entornos controlados
+
+## Usando LightEval para "Benchmarking"
+
+Las tareas de LightEval se definen usando un formato específico:
+``` 
+{suite}|{task}|{num_few_shot}|{auto_reduce} 
+```
+
+- **suite**: El conjunto de "benchmarks" (por ejemplo, 'mmlu', 'truthfulqa')
+- **task**: Tarea específica dentro del conjunto (por ejemplo, 'abstract_algebra')
+- **num_few_shot**: Número de ejemplos a incluir en el "prompt" (0 para zero-shot)
+- **auto_reduce**: Si se debe reducir automáticamente los ejemplos "few-shot" si el "prompt" es demasiado largo (0 o 1)
+
+Ejemplo: `"mmlu|abstract_algebra|0|0"` evalúa la tarea de álgebra abstracta de MMLU sin ejempls en el "prompt" (i.e. "zero-shot").
+
+### Ejemplo de Pipeline de Evaluación
+
+Aquí tienes un ejemplo completo de configuración y ejecución de una evaluación utilizando "benchmarks" automáticos relevantes para un dominio específico:
+
+```python
+from lighteval.tasks import Task, Pipeline
+from transformers import AutoModelForCausalLM
+
+# Definir tareas para evaluar
+domain_tasks = [
+    "mmlu|anatomy|0|0",
+    "mmlu|high_school_biology|0|0", 
+    "mmlu|high_school_chemistry|0|0",
+    "mmlu|professional_medicine|0|0"
+]
+
+# Configurar parámetros del pipeline
+pipeline_params = {
+    "max_samples": 40,  # Número de muestras para evaluar
+    "batch_size": 1,    # Tamaño del "batch" para la inferencia
+    "num_workers": 4    # Número de procesos paralelos
+}
+
+# Crear tracker de evaluación
+evaluation_tracker = EvaluationTracker(
+    output_path="./results",
+    save_generations=True
+)
+
+# Cargar modelo y crear pipeline
+model = AutoModelForCausalLM.from_pretrained("your-model-name")
+pipeline = Pipeline(
+    tasks=domain_tasks,
+    pipeline_parameters=pipeline_params,
+    evaluation_tracker=evaluation_tracker,
+    model=model
+)
+
+# Ejecutar evaluación
+pipeline.evaluate()
+
+# Obtener y mostrar resultados
+results = pipeline.get_results()
+pipeline.show_results()
+```
+
+Los resultados se muestran en formato tabular:
+``` 
+|                  Task                  |Version|Metric|Value |   |Stderr|
+|----------------------------------------|------:|------|-----:|---|-----:|
+|all                                     |       |acc   |0.3333|±  |0.1169|
+|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
+|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
+|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
+```
+
+También puedes manejar los resultados en un DataFrame de pandas y visualizarlos o representarlos como prefieras.
+
+# Próximos Pasos
+
+⏩ Explora [Evaluación Personalizada en un Dominio](./custom_evaluation.md) para aprender a crear flujos de evaluación adaptados a tus necesidades específicas.
diff --git a/es/4_evaluation/custom_evaluation.md b/es/4_evaluation/custom_evaluation.md
new file mode 100644
index 00000000..b8d765ce
--- /dev/null
+++ b/es/4_evaluation/custom_evaluation.md
@@ -0,0 +1,132 @@
+# Evaluación Personalizada en un Dominio
+
+Aunque los "benchmarks" estándares proporcionan conocimiento relevante, muchas aplicaciones requieren enfoques de evaluación especializados que se adapten a dominios específicos o a casos de uso particulares. Esta guía te ayudará a crear flujos de evaluación personalizados que evalúen con precisión el rendimiento de tu modelo en tu dominio objetivo.
+
+## Diseñando Tu Estrategia de Evaluación
+
+Una estrategia de evaluación personalizada exitosa comienza con objetivos claros. Es fundamental considerar qué capacidades específicas necesita demostrar tu modelo en tu dominio. Esto podría incluir conocimientos técnicos, patrones de razonamiento o formatos específicos del dominio. Documenta estos requisitos cuidadosamente; ellos guiarán tanto el diseño de tus tareas como la selección de métricas.
+
+Tu evaluación debe probar tanto casos de uso estándar como casos límite. Por ejemplo, en un dominio médico, podrías evaluar tanto escenarios comunes de diagnóstico como condiciones raras. En aplicaciones financieras, podrías probar tanto transacciones rutinarias como casos complejos que involucren múltiples monedas o condiciones especiales.
+
+## Implementación con LightEval
+
+LightEval proporciona un marco flexible para implementar evaluaciones personalizadas. Así es como puedes crear una tarea personalizada:
+
+```python
+from lighteval.tasks import Task, Doc
+from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
+
+class CustomEvalTask(Task):
+    def __init__(self):
+        super().__init__(
+            name="custom_task",
+            version="0.0.1",
+            metrics=["accuracy", "f1"],  # Tus métricas elegidas
+            description="Descripción de tu tarea de evaluación personalizada"
+        )
+    
+    def get_prompt(self, sample):
+        # Formatea tu entrada en un "prompt"
+        return f"Question: {sample['question']}\nAnswer:"
+    
+    def process_response(self, response, ref):
+        # Procesa el "output" del modelo y compáralo con la referencia
+        return response.strip() == ref.strip()
+```
+
+## Métricas Personalizadas
+
+Las tareas específicas de dominio a menudo requieren métricas especializadas. LightEval proporciona un marco flexible para crear métricas personalizadas que capturen aspectos relevantes del rendimiento del dominio:
+
+```python
+from aenum import extend_enum
+from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
+import numpy as np
+
+# Definir una función de métrica a nivel de muestra
+def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
+    """Ejemplo de métrica que genera múltiples puntuaciones por muestra"""
+    response = predictions[0]
+    return {
+        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
+        "length_match": len(response) == len(formatted_doc.reference)
+    }
+
+# Crear una métrica que genere múltiples valores por muestra
+custom_metric_group = SampleLevelMetricGrouping(
+    metric_name=["accuracy", "length_match"],  # Nombres de submétricas
+    higher_is_better={  # define si valores más altos son mejores para cada métrica
+        "accuracy": True,
+        "length_match": True
+    },
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=custom_metric,
+    corpus_level_fn={  # define cómo agregar cada métrica
+        "accuracy": np.mean,
+        "length_match": np.mean
+    }
+)
+
+# Registrar la métrica en LightEval
+extend_enum(Metrics, "custom_metric_name", custom_metric_group)
+```
+
+Para casos más simples donde solo necesitas un valor por muestra:
+
+```python
+def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
+    """Ejemplo de métrica que genera una única puntuación por muestra"""
+    response = predictions[0]
+    return response == formatted_doc.choices[formatted_doc.gold_index]
+
+simple_metric_obj = SampleLevelMetric(
+    metric_name="simple_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=simple_metric,
+    corpus_level_fn=np.mean  # define cómo agregar resultados entre muestras
+)
+
+extend_enum(Metrics, "simple_metric", simple_metric_obj)
+```
+
+Una vez definas tus métricas personalizadas, puedes usarlas luego en tus tareas de evaluación haciendo referencia a ellas en la configuración de la tarea. Las métricas se calcularán automáticamente en todas las muestras y se agregarán según las funciones que especifiques.
+
+Para métricas más complejas, considera:
+- Usar metadatos en tus documentos formateados para ponderar o ajustar puntuaciones
+- Implementar funciones de agregación personalizadas para estadísticas a nivel de corpus
+- Agregar verificaciones de validación para las entradas de tus métricas
+- Documentar casos límite y comportamientos esperados
+
+Para un ejemplo completo de métricas personalizadas en acción, consulta nuestro [proyecto de evaluación de dominio](./project/README.md).
+
+## Creación de Conjuntos de Datos
+
+La evaluación de alta calidad requiere conjuntos de datos cuidadosamente curados. Considera estos enfoques para la creación de conjuntos de datos:
+
+1. **Anotación por Expertos**: Trabaja con expertos del dominio para crear y validar ejemplos de evaluación. Herramientas como [Argilla](https://github.com/argilla-io/argilla) hacen este proceso más eficiente.
+
+2. **Datos del Mundo Real**: Recopila y anonimiza datos de uso real, asegurándote de que representen escenarios reales de despliegue del modelo.
+
+3. **Generación Sintética**: Usa LLMs para generar ejemplos iniciales y luego permite que expertos los validen y refinen.
+
+## Mejores Prácticas
+
+- Documenta tu metodología de evaluación a fondo, incluidas los supuestos o limitaciones
+- Incluye casos de prueba diversos que cubran diferentes aspectos de tu dominio
+- Considera tanto métricas automatizadas como evaluaciones humanas donde sea apropiado
+- Controla las versiones de tus conjuntos de datos y código de evaluación
+- Actualiza regularmente tu conjunto de evaluaciones a medida que descubras nuevos casos límite o requisitos
+
+## Referencias
+
+- [Guía de Tareas Personalizadas en LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Métricas Personalizadas en LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Documentación de Argilla](https://docs.argilla.io) para anotación de conjuntos de datos
+- [Guía de Evaluación](https://github.com/huggingface/evaluation-guidebook) para principios generales de evaluación
+
+# Próximos Pasos
+
+⏩ Para un ejemplo completo de cómo implementar estos conceptos, consulta nuestro [proyecto de evaluación de dominio](./project/README.md).
diff --git a/es/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/es/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
new file mode 100644
index 00000000..060c3262
--- /dev/null
+++ b/es/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
@@ -0,0 +1,19409 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# lighteval es tu biblioteca de evaluación de IA\n",
+        "\n",
+        "Este cuaderno explora cómo puedes usar lighteval para evaluar y comparar modelos de lenguaje grandes (LLMs).\n",
+        "\n",
+        "`lighteval` ha estado disponible por un tiempo y es una gran herramienta para obtener puntuaciones de evaluación en los principales \"benchmarks\". Recientemente ha sido refactorizado para poder usarse como una biblioteca en Python, lo que la hace excelente para comparar modelos en diferentes \"benchmarks\".\n",
+        "\n",
+        "Así que exploremos algunas puntuaciones de evaluación.\n",
+        "\n",
+        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+        "    <h2 style='margin: 0;color:blue'>Ejercicio: Evalúa tu propio modelo</h2>\n",
+        "    <p>Ahora que has visto cómo evaluar modelos en dominios específicos, intenta evaluar un modelo en un dominio que te interese.</p>\n",
+        "    <p><b>Niveles de dificultad</b></p>\n",
+        "    <p>🐢 Usa las tareas existentes del dominio médico pero evalúa un modelo diferente del repositorio de Hugging Face</p>\n",
+        "    <p>🐕 Crea una nueva evaluación de dominio seleccionando diferentes tareas de MMLU (por ejemplo, ciencias de la computación, matemáticas, física)</p>\n",
+        "    <p>🦁 Crea una tarea de evaluación personalizada utilizando el marco de tareas de LightEval y evalúa modelos en tu dominio específico</p>\n",
+        "</div>\n",
+        "\n",
+        "## Instalar dependencias"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "afW_CLJoPCnF",
+        "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n",
+        "!pip list | grep torch"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8xMnn_cQ1EEi",
+        "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Configurar la evaluación con `lighteval`\n",
+        "\n",
+        "Necesitamos configurar el entorno de evaluación y el pipeline. Muchas de estas configuraciones estarán desactivadas porque trabajaremos directamente en el notebook, pero también podríamos usar `push_to_hub` o `push_to_tensorboard`.\n",
+        "\n",
+        "### `push_to_hub`\n",
+        "\n",
+        "Esto es útil si estamos evaluando un modelo y queremos guardar su evaluación junto con los pesos y la configuración en el repositorio de Hugging Face.\n",
+        "\n",
+        "### `push_to_tensorboard`\n",
+        "\n",
+        "Esto sería útil si estamos construyendo una herramienta o script de evaluación y queremos visualizar los resultados en tensorboard.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296,
+          "referenced_widgets": [
+            "711ff69b059c421f84405dc798c84297",
+            "3c90ad18d29042caa880291056159985",
+            "66d9640217374065a9af74765929d326",
+            "5965b90591764931a635cc9305f2fa40",
+            "b34849662b994d87a912fdee127e1eed",
+            "1c358b2f428a4bb9bc0245b94d6394e8",
+            "9e9b42ee82614d56ab89f79bc2639d29",
+            "39db106760854445bc1d1c0a246400f9",
+            "737225a54b724c60ac0fb543b94bd39a",
+            "b6b2341d52504399840f08eb6d25bd84",
+            "4d137ae7c6e442f380e7764a741572d6",
+            "f837792155cc4e93884c80bd0bad7881",
+            "0ed8df4245654e928f9c9b584e02e203",
+            "2b426e2817f84313bbaf6c8f78ff3150",
+            "0ade751a6028417aa6f305217007bc64",
+            "5b2d53ff4a0a40f280757c5b396c2d88",
+            "b1201caf6e9d43a0b44f93307c248085",
+            "4fe67e045c8e4123beff0f1fb603cc0f",
+            "f292cff5aecd4010af6d20b8ba4d319d",
+            "4e4b570bb15b4f23b2340e5e2eb5622e",
+            "6b323ba03e1041299fb6a4e9d917027f",
+            "ceca2dcda90c444aa2e909e7f7109a53",
+            "f7c7eac52fde4cf5bd8c462765f0b8f9",
+            "d158043b045345b19594a903a43dbcf7",
+            "002f933919c148489c0667ac03f944f4",
+            "8ea9f11530f74a0e91a4200ba7f40ddb",
+            "c8db56b43c9342e392b86ddfac68f8f2",
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+          ]
+        },
+        "id": "3cUebd-z6IWs",
+        "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e"
+      },
+      "outputs": [],
+      "source": [
+        "import lighteval\n",
+        "import os\n",
+        "from datetime import timedelta\n",
+        "from transformers import AutoModelForCausalLM\n",
+        "\n",
+        "from lighteval.logging.evaluation_tracker import EvaluationTracker\n",
+        "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n",
+        "from lighteval.models.model_config import create_model_config\n",
+        "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n",
+        "\n",
+        "TOKEN = os.getenv(\"HF_TOKEN\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "muikmXNQXgFv"
+      },
+      "outputs": [],
+      "source": [
+        "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n",
+        "\n",
+        "evaluation_tracker = EvaluationTracker(\n",
+        "    output_dir=\"~/tmp\",\n",
+        "    save_details=False,\n",
+        "    push_to_hub=False,\n",
+        "    push_to_tensorboard=False,\n",
+        "    public=False,\n",
+        "    hub_results_org=False,\n",
+        ")\n",
+        "\n",
+        "pipeline_params = PipelineParameters(\n",
+        "    launcher_type=ParallelismManager.ACCELERATE,\n",
+        "    env_config=env_config,\n",
+        "    job_id=1,\n",
+        "    override_batch_size=1,\n",
+        "    num_fewshot_seeds=0,\n",
+        "    max_samples=10,\n",
+        "    use_chat_template=False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Comparar modelos con `lighteval`\n",
+        "\n",
+        "Vamos a comparar dos modelos pequeños en un dominio específico. Usaremos `Qwen2.5-0.5B` y `SmolLM2-360M-Instruct`, y los evaluaremos en un dominio médico.\n",
+        "\n",
+        "Podemos crear una evaluación de dominio a partir de un subconjunto de evaluaciones de MMLU definiendo las tareas de evaluación. En lighteval, las tareas se describen como cadenas.\n",
+        "\n",
+        "`{suite}|{task}:{subtask}|{num_few_shot}|{0 o 1 para reducir num_few_shot si el \"prompt\" es demasiado largo}`\n",
+        "\n",
+        "Por lo tanto, pasaremos nuestra lista de tareas relacionadas con medicina de esta forma:\n",
+        "\n",
+        "```\n",
+        "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
+        "```\n",
+        "\n",
+        "Que se traduce a:\n",
+        "\n",
+        "| Suite | Tarea | Ejemplos Fewshot | Limitar Fewshots |\n",
+        "|---|---|---|---|\n",
+        "| leaderboard | mmlu:anatomy | 5 | Falso |\n",
+        "| leaderboard | mmlu:professional_medicine | 5 | Falso |\n",
+        "| leaderboard | mmlu:high_school_biology | 5 | Falso |\n",
+        "| leaderboard | mmlu:high_school_chemistry | 5 | Falso |\n",
+        "\n",
+        "Para obtener una lista completa de las tareas soportadas por lighteval, consulta esta página en [la documentación](https://github.com/huggingface/lighteval/wiki/Available-Tasks).\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qTqsizv9mVbO"
+      },
+      "outputs": [],
+      "source": [
+        "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XwcJklSFX4H6"
+      },
+      "source": [
+        "# Evaluación Qwen2.5 0.5B"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
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+          ]
+        },
+        "id": "PXJuaXVxUNBO",
+        "outputId": "87aca361-5bd5-4b12-9097-3e97f1165d7b"
+      },
+      "outputs": [],
+      "source": [
+        "qwen_model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen2.5-0.5B\")\n",
+        "\n",
+        "pipeline = Pipeline(\n",
+        "    tasks=domain_tasks,\n",
+        "    pipeline_parameters=pipeline_params,\n",
+        "    evaluation_tracker=evaluation_tracker,\n",
+        "    model=qwen_model\n",
+        ")\n",
+        "\n",
+        "pipeline.evaluate()\n",
+        "\n",
+        "qwen_results = pipeline.get_results()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CIwCaCxJX_hA"
+      },
+      "source": [
+        "# Evaluación SmolLM 360M"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+            "9e9af55b08c648c88a0b6302c55d7936",
+            "1c6481ce4cf9499299683d593e2511d1"
+          ]
+        },
+        "id": "Dxg0RtlNVT4y",
+        "outputId": "ee40393c-bf62-499d-d443-e96da72411e8"
+      },
+      "outputs": [],
+      "source": [
+        "smol_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n",
+        "\n",
+        "pipeline = Pipeline(\n",
+        "    tasks=domain_tasks,\n",
+        "    pipeline_parameters=pipeline_params,\n",
+        "    evaluation_tracker=evaluation_tracker,\n",
+        "    model=smol_model\n",
+        ")\n",
+        "\n",
+        "pipeline.evaluate()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pdjyzfKHVt52"
+      },
+      "outputs": [],
+      "source": [
+        "smol_results = pipeline.get_results()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eugvMFfgV1VD",
+        "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96"
+      },
+      "outputs": [],
+      "source": [
+        "pipeline.show_results()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0HD8aFwSYGHu"
+      },
+      "source": [
+        "# Visualiza los Resultados\n",
+        "\n",
+        "Ahora que tenemos los resultados de los dos modelos, podemos visualizarlos lado a lado. Mantendremos la visualización simple, pero con esta estructura de datos podrías representar las puntuaciones de muchas maneras."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "1sReqrgQUO9r",
+        "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n",
+        "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n",
+        "df = pd.concat([df, _df], axis=1)\n",
+        "df.plot(kind=\"barh\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 💐 ¡Eso es todo!\n",
+        "\n",
+        "Tienes un cuaderno práctico para visualizar las evaluaciones de modelos. Puedes usarlo para:\n",
+        "\n",
+        "- seleccionar el modelo adecuado para tu caso de uso\n",
+        "- evaluar \"checkpoints\" durante el entrenamiento\n",
+        "- compartir puntuaciones de modelos\n",
+        "\n",
+        "🏃 Próximos pasos\n",
+        "\n",
+        "- Si quieres profundizar en tus resultados de evaluación, consulta este [cuaderno](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)."
+      ]
+    }
+  ],
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+    "accelerator": "GPU",
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+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/es/4_evaluation/project/README.md b/es/4_evaluation/project/README.md
new file mode 100644
index 00000000..70f9a9b2
--- /dev/null
+++ b/es/4_evaluation/project/README.md
@@ -0,0 +1,83 @@
+# Evaluación Específica en un Dominio con Argilla, Distilabel y LightEval
+
+La mayoría de los "benchmarks" populares evalúan capacidades muy generales (razonamiento, matemáticas, código), pero ¿alguna vez has necesitado estudiar capacidades más específicas?
+
+¿Qué deberías hacer si necesitas evaluar un modelo en un **dominio específico** relevante para tus casos de uso? (Por ejemplo, aplicaciones financieras, legales o médicas).
+
+Este tutorial muestra todo el flujo de trabajo que puedes seguir, desde la creación de datos relevantes y la anotación de tus muestras hasta la evaluación de tu modelo, utilizando las herramientas de [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel) y [lighteval](https://github.com/huggingface/lighteval). Para nuestro ejemplo, nos centraremos en generar preguntas de exámenes a partir de múltiples documentos.
+
+## Estructura del Proyecto
+
+Seguiremos 4 pasos, con un script para cada uno: generar un conjunto de datos, anotarlo, extraer muestras relevantes para la evaluación y, finalmente, evaluar los modelos.
+
+| Nombre del Script       | Descripción                                                                 |
+|-------------------------|-----------------------------------------------------------------------------|
+| generate_dataset.py     | Genera preguntas de exámenes a partir de múltiples documentos de texto usando un modelo de lenguaje especificado. |
+| annotate_dataset.py     | Crea un conjunto de datos en Argilla para la anotación manual de las preguntas generadas. |
+| create_dataset.py       | Procesa los datos anotados desde Argilla y crea una base de datos de Hugging Face. |
+| evaluation_task.py      | Define una tarea personalizada en LightEval para evaluar modelos de lenguaje en el conjunto de preguntas de examen. |
+
+## Pasos
+
+### 1. Generar el Conjunto de Datos
+
+El script `generate_dataset.py` utiliza la biblioteca distilabel para generar preguntas de examen basadas en múltiples documentos de texto. Usa el modelo especificado (por defecto: Meta-Llama-3.1-8B-Instruct) para crear preguntas, respuestas correctas y respuestas incorrectas (llamadas "distractores"). Puedes agregar tus propios datos y también usar un modelo diferente.
+
+Para ejecutar la generación:
+
+```sh
+python generate_dataset.py --input_dir ubicacion/de/los/documentos --model_id id_del_modelo --output_path output_directory
+```
+
+Esto creará un [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) que contiene las preguntas de examen generadas para todos los documentos en el directorio de entrada.
+
+### 2. Anotar el Conjunto de Datos
+
+El script `annotate_dataset.py` toma las preguntas generadas y crea una base de datos en Argilla para su anotación. Este script configura la estructura de la base de datos y la completa con las preguntas y respuestas generadas, aleatorizando el orden de las respuestas para evitar sesgos. Una vez en Argilla, tú o un experto en el dominio pueden validar el conjunto de datos con las respuestas correctas.
+
+Verás respuestas sugeridas por el LLM en orden aleatorio, y podrás aprobar la respuesta correcta o seleccionar una diferente. La duración de este proceso dependerá de la escala de tu base de datos de evaluación, la complejidad de tus datos y la calidad de tu LLM. Por ejemplo, logramos crear 150 muestras en 1 hora en el dominio de aprendizaje por transferencia, utilizando Llama-3.1-70B-Instruct, principalmente aprobando respuestas correctas y descartando las incorrectas.
+
+Para ejecutar el proceso de anotación:
+
+```sh
+python annotate_dataset.py --dataset_path ubicacion/del/distiset --output_dataset_name nombre_de_los_datos_argilla
+```
+
+Esto creará un conjunto de datos en Argilla que puede ser utilizado para revisión y anotación manual.
+
+![argilla_dataset](./images/domain_eval_argilla_view.png)
+
+Si no estás usando Argilla, desplégalo localmente o en Spaces siguiendo esta [guía rápida](https://docs.argilla.io/latest/getting_started/quickstart/).
+
+### 3. Crear la Base de Datos
+
+El script `create_dataset.py` procesa los datos anotados desde Argilla y crea una base de datos en Hugging Face. El script incorpora tanto respuestas sugeridas como respuestas anotadas manualmente. El script creará una base de datos con la pregunta, posibles respuestas y el nombre de la columna para la respuesta correcta. Para crear la base de datos final:
+
+```sh
+huggingface_hub login
+python create_dataset.py --dataset_path nombre_de_los_datos_argilla --dataset_repo_id id_repo_hf
+```
+
+Esto enviará el conjunto de datos al Hugging Face Hub bajo el repositorio especificado. Puedes ver una base de datos de ejemplo en el hub [aquí](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train). La vista previa de la base de datos se ve así:
+
+![hf_dataset](./images/domain_eval_dataset_viewer.png)
+
+### 4. Tarea de Evaluación
+
+El script `evaluation_task.py` define una tarea personalizada en LightEval para evaluar modelos de lenguaje en el conjunto de preguntas de examen. Incluye una función de "prompt", una métrica de "accuracy" personalizada y la configuración de la tarea.
+
+Para evaluar un modelo utilizando lighteval con la tarea personalizada de preguntas de examen:
+
+```sh
+lighteval accelerate \
+    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
+    --tasks "community|exam_questions|0|0" \
+    --custom_tasks domain-eval/evaluation_task.py \
+    --output_dir "./evals"
+```
+
+Puedes encontrar guías detalladas en el wiki de lighteval sobre cada uno de estos pasos:
+
+- [Crear una Tarea Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Crear una Métrica Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Usar Métricas Existentes](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/es/4_evaluation/project/annotate_dataset.py b/es/4_evaluation/project/annotate_dataset.py
new file mode 100644
index 00000000..dadffc10
--- /dev/null
+++ b/es/4_evaluation/project/annotate_dataset.py
@@ -0,0 +1,129 @@
+import argparse
+import json
+from random import choices, sample
+
+import argilla as rg
+from distilabel.distiset import Distiset
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Annotate exam questions dataset using Argilla."
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="API key for Argilla",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="API URL for Argilla",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Path to the exam questions dataset",
+)
+parser.add_argument(
+    "--dataset_config",
+    type=str,
+    default="default",
+    help="Configuration of the dataset",
+)
+parser.add_argument(
+    "--dataset_split",
+    type=str,
+    default="train",
+    help="Split of the dataset to use",
+)
+parser.add_argument(
+    "--output_dataset_name",
+    type=str,
+    default="exam_questions",
+    help="Name of the output Argilla dataset",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Create Argilla dataset with the feedback task for validation
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+
+if client.datasets(args.output_dataset_name):
+    print(f"Deleting existing dataset '{args.output_dataset_name}'")
+    client.datasets(args.output_dataset_name).delete()
+
+settings = rg.Settings(
+    fields=[
+        rg.TextField("question"),
+        rg.TextField("answer_a"),
+        rg.TextField("answer_b"),
+        rg.TextField("answer_c"),
+        rg.TextField("answer_d"),
+    ],
+    questions=[
+        rg.LabelQuestion(
+            name="correct_answer",
+            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
+        ),
+        rg.TextQuestion(
+            name="improved_question",
+            description="Could you improve the question?",
+        ),
+        rg.TextQuestion(
+            name="improved_answer",
+            description="Could you improve the best answer?",
+        ),
+    ],
+)
+
+dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
+dataset.create()
+
+################################################################################
+# Load the Distiset and process and add records to Argilla dataset
+# We will validate that questions appear in random order to avoid bias
+# but we will show correct answers in the Argilla UI as suggestions.
+################################################################################
+
+distiset = Distiset.load_from_disk(args.dataset_path)
+answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
+dataset_records = []
+
+for exam in distiset[args.dataset_config][args.dataset_split]:
+    exam_json = json.loads(exam["generation"])["exam"]
+
+    for question in exam_json:
+        answer = question["answer"]
+        distractors = question["distractors"]
+        distractors = choices(distractors, k=3)
+        answers = distractors + [answer]
+        answers = sample(answers, len(answers))
+        suggestion_idx = answers.index(answer)
+        fields = dict(zip(answer_names, answers))
+        fields["question"] = question["question"]
+
+        record = rg.Record(
+            fields=fields,
+            suggestions=[
+                rg.Suggestion(
+                    question_name="correct_answer",
+                    value=answer_names[suggestion_idx],
+                )
+            ],
+        )
+        dataset_records.append(record)
+
+dataset.records.log(dataset_records)
+
+print(
+    f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla."
+)
diff --git a/es/4_evaluation/project/create_dataset.py b/es/4_evaluation/project/create_dataset.py
new file mode 100644
index 00000000..822e268e
--- /dev/null
+++ b/es/4_evaluation/project/create_dataset.py
@@ -0,0 +1,72 @@
+import argparse
+
+import argilla as rg
+from datasets import Dataset
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Create a Hugging Face dataset from annotated Argilla data."
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="API key for Argilla",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="API URL for Argilla",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Path to the Argilla dataset",
+)
+parser.add_argument(
+    "--dataset_repo_id",
+    type=str,
+    default="burtenshaw/exam_questions",
+    help="Hugging Face dataset repository ID",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Initialize Argilla client and load dataset
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+dataset = client.datasets(args.dataset_path)
+
+################################################################################
+# Process Argilla records
+################################################################################
+
+dataset_rows = []
+
+for record in dataset.records(with_suggestions=True, with_responses=True):
+    row = record.fields
+
+    if len(record.responses) == 0:
+        answer = record.suggestions["correct_answer"].value
+        row["correct_answer"] = answer
+    else:
+        for response in record.responses:
+            if response.question_name == "correct_answer":
+                row["correct_answer"] = response.value
+    dataset_rows.append(row)
+
+################################################################################
+# Create Hugging Face dataset and push to Hub
+################################################################################
+
+hf_dataset = Dataset.from_list(dataset_rows)
+hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
+
+print(f"Dataset has been successfully pushed to {args.dataset_repo_id}")
diff --git a/es/4_evaluation/project/evaluation_task.py b/es/4_evaluation/project/evaluation_task.py
new file mode 100644
index 00000000..397c75ad
--- /dev/null
+++ b/es/4_evaluation/project/evaluation_task.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+from lighteval.metrics.utils.metric_utils import (
+    SampleLevelMetric,
+    MetricCategory,
+    MetricUseCase,
+)
+
+################################################################################
+# Define the prompt function based on the structure of the dataset
+################################################################################
+
+
+def prompt_fn(line, task_name: str = None):
+    """Converts a dataset line to a Doc object for evaluation."""
+    instruction = "Choose the correct answer for the following exam question:"
+    return Doc(
+        task_name=task_name,
+        query=f"{instruction} {line['question']}",
+        choices=[
+            f" {line['answer_a']}",
+            f" {line['answer_b']}",
+            f" {line['answer_c']}",
+            f" {line['answer_d']}",
+        ],
+        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
+            line["correct_answer"]
+        ),
+        instruction=instruction,
+    )
+
+
+################################################################################
+# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
+# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List
+# Existing metrics can be imported from lighteval.metrics.metrics
+################################################################################
+
+
+def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
+    response = np.argmin(kwargs["choices_logprob"])
+    return response == formatted_doc.gold_index
+
+
+custom_metric = SampleLevelMetric(
+    metric_name="exam_question_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.MULTICHOICE,
+    use_case=MetricUseCase.NONE,
+    sample_level_fn=sample_level_fn,
+    corpus_level_fn=np.mean,
+)
+
+################################################################################
+# Define the task based on the prompt function and the custom metric
+# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
+################################################################################
+
+task = LightevalTaskConfig(
+    name="example",
+    prompt_function=prompt_fn,
+    suite=["community"],
+    hf_repo="burtenshaw/exam_questions",
+    hf_subset="default",
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    few_shots_split=None,
+    few_shots_select=None,
+    metric=[custom_metric],
+)
+
+# Add the task to TASKS_TABLE
+TASKS_TABLE = [task]
+
+# MODULE LOGIC
+if __name__ == "__main__":
+    print([t.name for t in TASKS_TABLE])
+    print(len(TASKS_TABLE))
+
+# lighteval accelerate \
+# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
+# "community|example|0|0" \
+# --custom-tasks "submitted_tasks/example.py" \
+# --output-dir "results"
diff --git a/es/4_evaluation/project/generate_dataset.py b/es/4_evaluation/project/generate_dataset.py
new file mode 100644
index 00000000..cb531d3f
--- /dev/null
+++ b/es/4_evaluation/project/generate_dataset.py
@@ -0,0 +1,167 @@
+import argparse
+import os
+from pydantic import BaseModel, Field
+from datasets import Dataset
+
+from distilabel.llms import InferenceEndpointsLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps.tasks import TextGeneration
+
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Generate exam questions from text files in a directory."
+)
+parser.add_argument(
+    "--model_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="Model ID for text generation",
+)
+parser.add_argument(
+    "--tokenizer_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="Tokenizer ID for text generation",
+)
+parser.add_argument(
+    "--input_dir",
+    type=str,
+    help="Directory containing input text files",
+    default="data",
+)
+parser.add_argument(
+    "--max_new_tokens",
+    type=int,
+    default=2048,
+    help="Maximum number of new tokens to generate",
+)
+parser.add_argument(
+    "--output_path",
+    type=str,
+    default="exam_questions_output",
+    help="Directory to save the generated datasets",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Load the documents
+# We assume that the documents are in the input directory, and that each file
+# is a separate document about the same topic.
+################################################################################
+
+# Process all text files in the input directory
+documents = []
+for filename in os.listdir(args.input_dir):
+    if filename.endswith(".txt"):
+        file_path = os.path.join(args.input_dir, filename)
+        with open(file=file_path, mode="r", encoding="utf-8") as file:
+            document_content = file.read()
+            documents.append(document_content)
+
+# Create a single dataset from all document contents
+dataset = Dataset.from_dict({"document": documents})
+
+################################################################################
+# Define the prompts
+# We use a system prompt to guide the model to generate the correct output format.
+# A template is used to insert the document into the prompt.
+################################################################################
+
+SYSTEM_PROMPT = """\
+You are an exam writer specialized in writing exams for students.
+Your goal is to create questions and answers based on the document provided, 
+and a list of distractors, that are incorrect but viable answers to the question.
+Your answer must adhere to the following format:
+```
+[
+    {
+        "question": "Your question",
+        "answer": "The correct answer to the question",
+        "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"]
+    },
+    ... (more questions and answers as required)
+]
+```
+""".strip()
+
+INSTRUCTION_TEMPLATE = """\
+    Generate a list of answers and questions about the document. 
+    Document:\n\n{{ instruction }}"""
+
+################################################################################
+# Define the output structure
+# We define a data model for the output of the pipeline, this is used to ensure
+# that the output is in the correct format for the evaluation task.
+################################################################################
+
+
+class ExamQuestion(BaseModel):
+    question: str = Field(..., description="The question to be answered")
+    answer: str = Field(..., description="The correct answer to the question")
+    distractors: List[str] = Field(
+        ..., description="A list of incorrect but viable answers to the question"
+    )
+
+
+class ExamQuestions(BaseModel):
+    exam: List[ExamQuestion]
+
+
+################################################################################
+# Create the pipeline
+# We create a pipeline with a single task that generates the exam questions
+# based on the document and in the correct format. We will Hugging Face
+# InferenceEndpoints and the model specified in the arguments.
+################################################################################
+
+with Pipeline(
+    name="Domain-Eval-Questions",
+    description="Generate exam questions based on given documents.",
+) as pipeline:
+    # Set up the text generation task
+    text_generation = TextGeneration(
+        name="exam_generation",
+        llm=InferenceEndpointsLLM(
+            model_id=args.model_id,
+            tokenizer_id=args.model_id,
+            api_key=os.environ["HF_TOKEN"],
+            structured_output={
+                "schema": ExamQuestions.model_json_schema(),
+                "format": "json",
+            },
+        ),
+        input_batch_size=8,
+        output_mappings={"model_name": "generation_model"},
+        input_mappings={"instruction": "document"},
+        system_prompt=SYSTEM_PROMPT,
+        template=INSTRUCTION_TEMPLATE,
+    )
+
+
+################################################################################
+# Run the pipeline
+# We run the pipeline for all documents and save the results to the output path.
+################################################################################
+
+if __name__ == "__main__":
+    # Run the pipeline for all documents
+    distiset = pipeline.run(
+        parameters={
+            "exam_generation": {
+                "llm": {
+                    "generation_kwargs": {
+                        "max_new_tokens": args.max_new_tokens,
+                    }
+                }
+            }
+        },
+        use_cache=False,
+        dataset=dataset,
+    )
+
+    distiset.save_to_disk(args.output_path)
diff --git a/es/4_evaluation/project/images/domain_eval_argilla_view.png b/es/4_evaluation/project/images/domain_eval_argilla_view.png
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HcmV?d00001


From df2dc6832ba994da2832a7ea7d897eadd0f5912c Mon Sep 17 00:00:00 2001
From: Knight7561 <rohithvr3@gmail.com>
Date: Wed, 18 Dec 2024 16:34:43 -0500
Subject: [PATCH 05/63] Fixing wrong args passed to DPOTrainer

---
 .../notebooks/dpo_finetuning_example.ipynb         | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
index be6116ad..a18d39e1 100644
--- a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -276,6 +276,13 @@
     "    use_mps_device=device == \"mps\",\n",
     "    # Model ID for HuggingFace Hub uploads\n",
     "    hub_model_id=finetune_name,\n",
+    "    # DPO-specific temperature parameter that controls the strength of the preference model\n",
+    "    # Lower values (like 0.1) make the model more conservative in following preferences\n",
+    "    beta=0.1,\n",
+    "    # Maximum length of the input prompt in tokens\n",
+    "    max_prompt_length=1024,\n",
+    "    # Maximum combined length of prompt + response in tokens\n",
+    "    max_length=1536\n",
     ")"
    ]
   },
@@ -294,13 +301,6 @@
     "    train_dataset=dataset,\n",
     "    # Tokenizer for processing inputs\n",
     "    processing_class=tokenizer,\n",
-    "    # DPO-specific temperature parameter that controls the strength of the preference model\n",
-    "    # Lower values (like 0.1) make the model more conservative in following preferences\n",
-    "    beta=0.1,\n",
-    "    # Maximum length of the input prompt in tokens\n",
-    "    max_prompt_length=1024,\n",
-    "    # Maximum combined length of prompt + response in tokens\n",
-    "    max_length=1536,\n",
     ")"
    ]
   },

From 52b645d78f97742b762823b676be746d0576e766 Mon Sep 17 00:00:00 2001
From: ViniFBN <81927867+ViniFBN@users.noreply.github.com>
Date: Fri, 20 Dec 2024 19:54:24 +0100
Subject: [PATCH 06/63] Module 4 - Translated

Conclusion of the translation regarding the fourth module
---
 .../notebooks/orpo_finetuning_example.ipynb   |     2 +-
 pt-br/4_evaluation/README.md                  |    40 +
 pt-br/4_evaluation/automatic_benchmarks.md    |   137 +
 pt-br/4_evaluation/custom_evaluation.md       |   132 +
 ...hteval_evaluate_and_analyse_your_LLM.ipynb | 19422 ++++++++++++++++
 pt-br/4_evaluation/project/README.md          |    85 +
 .../4_evaluation/project/annotate_dataset.py  |   129 +
 pt-br/4_evaluation/project/create_dataset.py  |    72 +
 pt-br/4_evaluation/project/evaluation_task.py |    86 +
 .../4_evaluation/project/generate_dataset.py  |   167 +
 .../images/domain_eval_argilla_view.png       |   Bin 0 -> 197693 bytes
 .../images/domain_eval_dataset_viewer.png     |   Bin 0 -> 89114 bytes
 pt-br/README.md                               |    13 +-
 13 files changed, 20278 insertions(+), 7 deletions(-)
 create mode 100644 pt-br/4_evaluation/README.md
 create mode 100644 pt-br/4_evaluation/automatic_benchmarks.md
 create mode 100644 pt-br/4_evaluation/custom_evaluation.md
 create mode 100644 pt-br/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
 create mode 100644 pt-br/4_evaluation/project/README.md
 create mode 100644 pt-br/4_evaluation/project/annotate_dataset.py
 create mode 100644 pt-br/4_evaluation/project/create_dataset.py
 create mode 100644 pt-br/4_evaluation/project/evaluation_task.py
 create mode 100644 pt-br/4_evaluation/project/generate_dataset.py
 create mode 100644 pt-br/4_evaluation/project/images/domain_eval_argilla_view.png
 create mode 100644 pt-br/4_evaluation/project/images/domain_eval_dataset_viewer.png

diff --git a/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
index 00eeb50c..8b1f766c 100644
--- a/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ b/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
@@ -86,7 +86,7 @@
     "# Load dataset\n",
     "\n",
     "# TODO: 🦁🐕 change the dataset to one of your choosing\n",
-    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")"
+    "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\")"
    ]
   },
   {
diff --git a/pt-br/4_evaluation/README.md b/pt-br/4_evaluation/README.md
new file mode 100644
index 00000000..a30d76ef
--- /dev/null
+++ b/pt-br/4_evaluation/README.md
@@ -0,0 +1,40 @@
+# Evaluation (Avaliação)
+
+A avaliação é uma etapa crítica no desenvolvimento e implantação de modelos de linguagem. Ela nos ajuda a entender quão bem nossos modelos desempenham em diferentes capacidades e a identificar áreas para melhorias. Este módulo aborda benchmarks padrão e abordagens de avaliação específicas de domínio para avaliar de forma abrangente o seu modelo smol (miudinho).
+
+Usaremos o [`lighteval`](https://github.com/huggingface/lighteval), uma poderosa biblioteca de avaliação desenvolvida pelo Hugging Face que se integra perfeitamente ao ecossistema Hugging Face. Para um aprendizado mais profundo nos conceitos e práticas recomendadas de avaliação, confira o [guia](https://github.com/huggingface/evaluation-guidebook).
+
+## Visão Geral do Módulo 
+
+Uma estratégia de avaliação completa examina múltiplos aspectos do desempenho do modelo. Avaliamos capacidades específicas de tarefas, como responder a perguntas e sumarização, para entender como o modelo lida com diferentes tipos de problemas. Medimos a qualidade do output através de fatores como coerência e precisão factual. A avaliação de segurança ajuda a identificar outputs potencialmente prejudiciais ou biases. Por fim, os testes de especialização de domínio verificam o conhecimento especializado do modelo no campo-alvo.
+
+## Conteúdo
+
+### 1️⃣ [Benchmarks Automáticos](./automatic_benchmarks.md)
+
+Aprenda a avaliar seu modelo usando benchmarks e métricas padronizados. Exploraremos benchmarks comuns, como MMLU e TruthfulQA, entenderemos as principais métricas e configurações de avaliação e abordaremos as melhores práticas para avaliações reproduzíveis.
+
+### 2️⃣ [Avaliação de Domnínio Personalizado](./custom_evaluation.md)
+
+Descubra como criar pipelines de avaliação adaptados ao seu caso de uso específico. Veremos o design de tarefas de avaliação personalizadas, implementação de métricas especializadas e construção de conjuntos de dados de avaliação que atendam às suas necessidades.
+
+### 3️⃣ [Projeto de Avaliação de Domínio](./project/README.md)
+
+Siga um exemplo completo de construção de um pipeline de avaliação de domínio específico. Você aprenderá a gerar conjuntos de dados de avaliação, usar o Argilla para anotação de dados, criar conjuntos de dados padronizados e avaliar modelos usando o LightEval.
+
+### Cadernos de Exercício
+
+| Título | Descrição | Exercício | Link | Colab |
+|-------|-------------|----------|------|-------|
+| Avalie e Analise Seu LLM | Aprenda a usar o LightEval para avaliar e comparar modelos em domínios específicos | 🐢 Use tarefas do domínio da medicina para avaliar um modelo <br> 🐕 Crie uma nova avaliação de domínio com diferentes tarefas do MMLU <br> 🦁 Crie uma tarefa de avaliação personalizada para o seu domínio | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+
+## Referências
+
+- [Guia de Avaliação](https://github.com/huggingface/evaluation-guidebook) - Guia abrangente de avaliação de LLMs
+- [Documentação do LightEval](https://github.com/huggingface/lighteval) - Documentação oficial do módulo LightEval
+- [Documentação do Argilla](https://docs.argilla.io) - Saiba mais sobre a plataforma de anotação Argilla
+- [Artigo do MMLU](https://arxiv.org/abs/2009.03300) - Artigo que descreve o benchmark MMLU
+- [Criando uma Tarefa Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Criando uma Métrica Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Usando Métricas Existentes](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
diff --git a/pt-br/4_evaluation/automatic_benchmarks.md b/pt-br/4_evaluation/automatic_benchmarks.md
new file mode 100644
index 00000000..0c2cc55d
--- /dev/null
+++ b/pt-br/4_evaluation/automatic_benchmarks.md
@@ -0,0 +1,137 @@
+# Benchmarks Automáticos
+
+Os benchmarks automáticos servem como ferramentas padronizadas para avaliar modelos de linguagem em diferentes tarefas e capacidades. Embora forneçam um ponto de partida útil para entender o desempenho do modelo, é importante reconhecer que eles representam apenas uma parte de uma estratégia abrangente de avaliação.
+
+## Entendendo os Benchmarks Automáticos
+
+Os benchmarks automáticos geralmente consistem em conjuntos de dados organizados com tarefas e métricas de avaliação predefinidas. Esses benchmarks têm como objetivo avaliar vários aspectos da capacidade do modelo, desde compreensão básica de linguagem até raciocínio complexo. A principal vantagem de usar benchmarks automáticos é a padronização, eles permitem comparações consistentes entre diferentes modelos e fornecem resultados reproduzíveis.
+
+No entanto, é crucial entender que o desempenho em benchmarks nem sempre se traduz diretamente em eficácia no mundo real. Um modelo que se destaca em benchmarks acadêmicos pode ainda enfrentar dificuldades em aplicações específicas de domínio ou casos práticos.
+
+## Benchmarks e Suas Limitações
+
+### Benchmarks de Conhecimento Geral
+
+O MMLU (Massive Multitask Language Understanding) testa conhecimentos em 57 disciplinas, de ciências a humanidades. Embora abrangente, este benchmark pode não refletir a profundidade de especialização necessária para domínios específicos. O TruthfulQA avalia a tendência de um modelo em reproduzir conceitos equivocados comuns, embora não capture todas as formas de desinformação.
+
+### Benchmarks de Raciocínio
+
+BBH (Big Bench Hard) e GSM8K focam em tarefas de raciocínio complexo. O BBH testa pensamento lógico e planejamento, enquanto o GSM8K visa especificamente a resolução de problemas matemáticos. Esses benchmarks ajudam a avaliar capacidades analíticas, mas podem não captar o raciocínio detalhado exigido em cenários do mundo real.
+
+### Compreensão de Linguagem
+
+HELM fornece uma estrutura de avaliação holística, enquanto o WinoGrande testa o senso comum através da desambiguação de pronomes. Esses benchmarks oferecem insights sobre capacidades de processamento de linguagem, mas podem não representar totalmente a complexidade da conversação natural ou terminologia específica de domínio.
+
+## Abordagens Alternativas de Avaliação
+
+Muitas organizações desenvolveram métodos alternativos de avaliação para lidar com as limitações dos benchmarks padrão:
+
+### LLM como Juiz
+
+Usar um modelo de linguagem para avaliar os outputs de outro tornou-se cada vez mais popular. Essa abordagem pode fornecer feedback mais detalhado do que métricas tradicionais, embora apresente seus próprios vieses e limitações.
+
+### Arenas de Avaliação
+
+Plataformas como a IA Arena Constitucional da Anthropic permitem que modelos interajam e avaliem uns aos outros em ambientes controlados. Isso pode revelar pontos fortes e fracos que podem não ser evidentes em benchmarks tradicionais.
+
+### Conjuntos de Benchmarks Personalizados
+
+As organizações frequentemente desenvolvem conjuntos de benchmarks internos adaptados às suas necessidades e casos de uso específicos. Esses conjuntos podem incluir testes de conhecimento de domínio ou cenários de avaliação que refletem condições reais de implantação.
+
+## Criando Sua Própria Estratégia de Avaliação
+
+Embora o LightEval facilite a execução de benchmarks padrão, você também deve investir tempo no desenvolvimento de métodos de avaliação específicos para o seu caso de uso.
+
+Sabendo que benchmarks padrão fornecem uma linha de base útil, eles não devem ser seu único método de avaliação. Aqui está como desenvolver uma abordagem mais abrangente:
+
+1. Comece com benchmarks padrão relevantes para estabelecer uma linha de base e permitir comparações com outros modelos.
+
+2. Identifique os requisitos específicos e desafios do seu caso de uso. Quais tarefas seu modelo realmente executará? Que tipos de erros seriam mais problemáticos?
+
+3. Desenvolva conjuntos de dados de avaliação personalizados que reflitam seu caso de uso real. Isso pode incluir:
+    - Consultas reais de usuários do seu domínio
+    - Casos extremos comuns que você encontrou
+    - Exemplos de cenários particularmente desafiadores
+
+4. Considere implementar uma estratégia de avaliação em camadas:
+    - Métricas automatizadas para feedback rápido
+    - Avaliação humana para entendimento mais detalhado
+    - Revisão por especialistas no domínio para aplicações especializadas
+    - Testes A/B em ambientes controlados
+
+## Usando LightEval para Benchmarks
+
+As tarefas do LightEval são definidas usando um formato específico:
+
+```
+{suite}|{task}|{num_few_shot}|{auto_reduce}
+```
+
+- **suite**: O conjunto de benchmarks (ex.: 'mmlu', 'truthfulqa')
+- **task**: Tarefa específica dentro do conjunto (ex.: 'abstract_algebra')
+- **num_few_shot**: Número de exemplos a incluir no prompt (0 para zero-shot)
+- **auto_reduce**: Se deve reduzir automaticamente exemplos few-shot caso o prompt seja muito longo (0 ou 1)
+
+Exemplo: `"mmlu|abstract_algebra|0|0"` avalia a tarefa de álgebra abstrata do MMLU com inferência zero-shot.
+
+### Exemplo de Pipeline de Avaliação
+
+Aqui está um exemplo completo de configuração e execução de uma avaliação em benchmarks automáticos relevantes para um domínio específico:
+
+```python
+from lighteval.tasks import Task, Pipeline
+from transformers import AutoModelForCausalLM
+
+# Define tasks to evaluate
+domain_tasks = [
+    "mmlu|anatomy|0|0",
+    "mmlu|high_school_biology|0|0", 
+    "mmlu|high_school_chemistry|0|0",
+    "mmlu|professional_medicine|0|0"
+]
+
+# Configure pipeline parameters
+pipeline_params = {
+    "max_samples": 40,  # Number of samples to evaluate
+    "batch_size": 1,    # Batch size for inference
+    "num_workers": 4    # Number of worker processes
+}
+
+# Create evaluation tracker
+evaluation_tracker = EvaluationTracker(
+    output_path="./results",
+    save_generations=True
+)
+
+# Load model and create pipeline
+model = AutoModelForCausalLM.from_pretrained("your-model-name")
+pipeline = Pipeline(
+    tasks=domain_tasks,
+    pipeline_parameters=pipeline_params,
+    evaluation_tracker=evaluation_tracker,
+    model=model
+)
+
+# Run evaluation
+pipeline.evaluate()
+
+# Get and display results
+results = pipeline.get_results()
+pipeline.show_results()
+```
+
+Os resultados são exibidos em formato tabular, mostrando:
+```
+|                  Task                  |Version|Metric|Value |   |Stderr|
+|----------------------------------------|------:|------|-----:|---|-----:|
+|all                                     |       |acc   |0.3333|±  |0.1169|
+|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
+|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
+|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
+```
+
+Você também pode manipular os resultados em um DataFrame do pandas e visualizá-los conforme necessário.
+
+# Próximos Passos
+
+⏩ Veja a [Avaliação Personalizada de Domínio](./custom_evaluation.md) para aprender a criar pipelines de avaliação adaptados às suas necessidades específicas.
diff --git a/pt-br/4_evaluation/custom_evaluation.md b/pt-br/4_evaluation/custom_evaluation.md
new file mode 100644
index 00000000..1b7bdc40
--- /dev/null
+++ b/pt-br/4_evaluation/custom_evaluation.md
@@ -0,0 +1,132 @@
+# Avaliação de Domínio Personalizado
+
+Embora benchmarks padrão ofereçam insights valiosos, muitas aplicações requerem abordagens de avaliação especializadas adaptadas a domínios ou casos de uso específicos. Este guia ajudará você a criar pipelines de avaliação personalizados que avaliem com precisão o desempenho do seu modelo no domínio-alvo.
+
+## Planejando Sua Estratégia de Avaliação
+
+Uma estratégia de avaliação personalizada bem-sucedida começa com objetivos claros. Considere quais capacidades específicas seu modelo precisa demonstrar no seu domínio. Isso pode incluir conhecimento técnico, padrões de raciocínio ou formatos específicos de domínio. Documente esses requisitos cuidadosamente, eles guiarão tanto o design da tarefa quanto a seleção de métricas.
+
+Sua avaliação deve testar tanto casos de uso padrão quanto casos extremos. Por exemplo, em um domínio de medicina, você pode avaliar cenários com diagnósticos comuns e condições raras. Em aplicações financeiras, pode-se testar transações rotineiras e casos extremos envolvendo múltiplas moedas ou condições especiais.
+
+## Implementação com LightEval
+
+O LightEval fornece um framework flexível para implementar avaliações personalizadas. Veja como criar uma tarefa personalizada:
+
+```python
+from lighteval.tasks import Task, Doc
+from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
+
+class CustomEvalTask(Task):
+    def __init__(self):
+        super().__init__(
+            name="custom_task",
+            version="0.0.1",
+            metrics=["accuracy", "f1"],  # Your chosen metrics
+            description="Description of your custom evaluation task"
+        )
+    
+    def get_prompt(self, sample):
+        # Format your input into a prompt
+        return f"Question: {sample['question']}\nAnswer:"
+    
+    def process_response(self, response, ref):
+        # Process model output and compare to reference
+        return response.strip() == ref.strip()
+```
+
+## Métricas Personalizadas
+
+Tarefas específicas de domínio frequentemente exigem métricas especializadas. O LightEval fornece um framework flexível para criar métricas personalizadas que capturam aspectos relevantes do desempenho no domínio:
+
+```python
+from aenum import extend_enum
+from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
+import numpy as np
+
+# Define a sample-level metric function
+def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
+    """Example metric that returns multiple scores per sample"""
+    response = predictions[0]
+    return {
+        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
+        "length_match": len(response) == len(formatted_doc.reference)
+    }
+
+# Create a metric that returns multiple values per sample
+custom_metric_group = SampleLevelMetricGrouping(
+    metric_name=["accuracy", "length_match"],  # Names of sub-metrics
+    higher_is_better={  # Whether higher values are better for each metric
+        "accuracy": True,
+        "length_match": True
+    },
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=custom_metric,
+    corpus_level_fn={  # How to aggregate each metric
+        "accuracy": np.mean,
+        "length_match": np.mean
+    }
+)
+
+# Register the metric with LightEval
+extend_enum(Metrics, "custom_metric_name", custom_metric_group)
+```
+
+Para casos mais simples onde você precisa de apenas um valor por amostra:
+
+```python
+def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
+    """Example metric that returns a single score per sample"""
+    response = predictions[0]
+    return response == formatted_doc.choices[formatted_doc.gold_index]
+
+simple_metric_obj = SampleLevelMetric(
+    metric_name="simple_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=simple_metric,
+    corpus_level_fn=np.mean  # How to aggregate across samples
+)
+
+extend_enum(Metrics, "simple_metric", simple_metric_obj)
+```
+
+Você pode então usar suas métricas personalizadas em tarefas de avaliação referenciando-as na configuração da tarefa. As métricas serão automaticamente calculadas para todas as amostras e agregadas de acordo com as funções especificadas.
+
+Para métricas mais complexas, considere:
+- Usar metadados em seus documentos formatados para ponderar ou ajustar pontuações
+- Implementar funções de agregação personalizadas para estatísticas em nível de corpus
+- Adicionar verificações de validação para as entradas da métrica
+- Documentar casos extremos e comportamentos esperados
+
+Para um exemplo completo de métricas personalizadas em ação, veja nosso [projeto de avaliação de domínio](./project/README.md).
+
+## Criação de Conjuntos de Dados
+
+Uma avaliação de alta qualidade requer conjuntos de dados cuidadosamente selecionados. Considere estas abordagens para criação de conjuntos de dados:
+
+1. Anotação Avançada: Trabalhe com especialistas no domínio para criar e validar exemplos de avaliação. Ferramentas como o [Argilla](https://github.com/argilla-io/argilla) tornam esse processo mais eficiente.
+
+2. Dados do Mundo Real: Colete e anonimize dados de uso real, garantindo que representem cenários reais de implantação.
+
+3. Geração Sintética: Use LLMs para gerar exemplos iniciais e, em seguida, peça para especialistas validarem e refinarem esses exemplos.
+
+## Melhores Práticas
+
+- Documente sua metodologia de avaliação detalhadamente, incluindo quaisquer suposições ou limitações
+- Inclua casos de teste diversificados que cubram diferentes aspectos do seu domínio
+- Considere métricas automatizadas e avaliação humana, quando apropriado
+- Faça o controle de versão dos seus conjuntos de dados e código de avaliação
+- Atualize regularmente seu conjunto de avaliação à medida que descobrir novos casos extremos ou requisitos
+
+## Referências
+
+- [Guia de Tarefas Personalizadas do LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Métricas Personalizadas do LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Documentação do Argilla](https://docs.argilla.io) para anotação de conjuntos de dados
+- [Guia de Avaliação](https://github.com/huggingface/evaluation-guidebook) para princípios gerais de avaliação
+
+# Próximos Passos
+
+⏩ Para um exemplo completo de implementação desses conceitos, veja nosso [projeto de avaliação de domínio](./project/README.md).
\ No newline at end of file
diff --git a/pt-br/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/pt-br/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
new file mode 100644
index 00000000..1677ea01
--- /dev/null
+++ b/pt-br/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
@@ -0,0 +1,19422 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TZVw9f5QYWPL"
+      },
+      "source": [
+        "# lighteval é o seu módulo de avaliação de IA\n",
+        "\n",
+        "Este caderno explora como você pode usar o lighteval para avaliar e comparar LLMs.\n",
+        "\n",
+        "O `lighteval` já existe há algum tempo e é uma ótima ferramenta para obter pontuações de avaliação em benchmarks importantes. Ele foi recentemente reformulado para ser usado como uma biblioteca em Python, o que o torna excelente para comparar modelos em diferentes benchmarks.\n",
+        "\n",
+        "Então, vamos mergulhar nas pontuações de avaliação.\n",
+        "\n",
+        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+        "    <h2 style='margin: 0;color:blue'>Exercício: Avalie Seu Próprio Modelo</h2>\n",
+        "    <p>Agora que você viu como avaliar modelos em domínios específicos, experimente avaliar um modelo de um domínio que lhe interessa.</p>\n",
+        "    <p><b>Níveis de Dificuldade</b></p>\n",
+        "    <p>🐢 Use as tarefas existentes do domínio médico, mas avalie um modelo diferente do hub do Hugging Face</p>\n",
+        "    <p>🐕 Crie uma nova avaliação de domínio selecionando diferentes tarefas do MMLU (por exemplo, ciência da computação, matemática, física)</p>\n",
+        "    <p>🦁 Crie uma tarefa de avaliação personalizada usando o framework de tarefas do LightEval e avalie modelos no seu domínio específico</p>\n",
+        "</div>\n",
+        "\n",
+        "## Instalar dependências"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "afW_CLJoPCnF",
+        "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n",
+        "!pip list | grep torch"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8xMnn_cQ1EEi",
+        "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TDKs5ShvXw8K"
+      },
+      "source": [
+        "## Configurar a avaliação com `lighteval`\n",
+        "\n",
+        "Precisamos configurar o ambiente e o pipeline de avaliação. Muito disso será desativado porque estamos mantendo as coisas no notebook, mas também podemos usar `push_to_hub` ou `push_to_tensorboard`.\n",
+        "\n",
+        "### `push_to_hub`\n",
+        "\n",
+        "Isso é útil se estivermos avaliando um modelo e quisermos persistir sua avaliação com pesos e configuração no hub do Hugging Face.\n",
+        "\n",
+        "### `push_to_tensorboard`\n",
+        "\n",
+        "Isso é útil se estivermos criando uma ferramenta ou script de avaliação, onde queremos visualizar os resultados no TensorBoard.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296,
+          "referenced_widgets": [
+            "711ff69b059c421f84405dc798c84297",
+            "3c90ad18d29042caa880291056159985",
+            "66d9640217374065a9af74765929d326",
+            "5965b90591764931a635cc9305f2fa40",
+            "b34849662b994d87a912fdee127e1eed",
+            "1c358b2f428a4bb9bc0245b94d6394e8",
+            "9e9b42ee82614d56ab89f79bc2639d29",
+            "39db106760854445bc1d1c0a246400f9",
+            "737225a54b724c60ac0fb543b94bd39a",
+            "b6b2341d52504399840f08eb6d25bd84",
+            "4d137ae7c6e442f380e7764a741572d6",
+            "f837792155cc4e93884c80bd0bad7881",
+            "0ed8df4245654e928f9c9b584e02e203",
+            "2b426e2817f84313bbaf6c8f78ff3150",
+            "0ade751a6028417aa6f305217007bc64",
+            "5b2d53ff4a0a40f280757c5b396c2d88",
+            "b1201caf6e9d43a0b44f93307c248085",
+            "4fe67e045c8e4123beff0f1fb603cc0f",
+            "f292cff5aecd4010af6d20b8ba4d319d",
+            "4e4b570bb15b4f23b2340e5e2eb5622e",
+            "6b323ba03e1041299fb6a4e9d917027f",
+            "ceca2dcda90c444aa2e909e7f7109a53",
+            "f7c7eac52fde4cf5bd8c462765f0b8f9",
+            "d158043b045345b19594a903a43dbcf7",
+            "002f933919c148489c0667ac03f944f4",
+            "8ea9f11530f74a0e91a4200ba7f40ddb",
+            "c8db56b43c9342e392b86ddfac68f8f2",
+            "92d72302ec504f3c8f9546cef7723d75",
+            "169354e13fc8418ca386911da39511f8",
+            "61543cdfbeb147f4a75366d7bcde1fc0",
+            "986bbdd11ad94de194185530660a6953",
+            "17889c7b25df47b7b988c6b6b7b5c83a",
+            "186e4b1bdb844c769ed171b00a2bd66c",
+            "d2f11824da4a49eb9f4bc912247b2eb4",
+            "59f8e743cc7a48f9a7112962a7ec98c9",
+            "05bf3bda35b048869e34bed2225a34d4",
+            "8a0d497c293348678a64e22fb2f12d49",
+            "6c7d9ef6ea40439e9f0667f72e27e18d",
+            "f6a50efc22f54714af17113da7acd02b",
+            "1e2a39609cb34dc8b5f29c4d57a95746",
+            "22a4c40951984de0b0e8a71786119997",
+            "fa835fc797194aff91961cddad559e44",
+            "de11455c9560467b921f13a51c02b914",
+            "9d9d37d6faf04bfe9618a851c986daff",
+            "ec76a28c1d7a41b2926527ea45a4e574",
+            "051f5d56561f4ce49621fb8bb9d2a005",
+            "4a664ec8153b48458553d1c8757a7842",
+            "6c1692a29a814dc7b7d4b0333dd56627",
+            "10e14de256084f96957e2cb68034ce38",
+            "90e6bd13f39b4109922b2504dff7cd93",
+            "e551833c9d1640d98a698e82e17faffb",
+            "ecf5eca6b8d64175a39f9fb8d239f6fd",
+            "6ec6def8ee894aed8648d4d9464c64dd",
+            "09aef2e473464b85b7c5e2d124de83e5",
+            "3ebbb1cf8ac846e28d6a611c144abd02",
+            "47de67c7c71a4b09a010d6e5e7230a4c",
+            "be5933b10a074ade99632e5da6836820",
+            "863d305f19ef49b79ea62a27d94508cd",
+            "5ed5a979f7a54dfa9980717329a5c496",
+            "e82245d7d8c742ed8b8efe515750f6fc",
+            "be5fb5250a2e442cb767866442fdd4df",
+            "2ddf3728840144949e3b23521833c9ae",
+            "68cb76f44f5446d28fd4895a5cfbfee2",
+            "7d0f948b4ec7480abf9900370f698581",
+            "598f3d372159464181ea84e1bb5bcf86",
+            "18a28139ade541f2938042fff9ad4ec8"
+          ]
+        },
+        "id": "3cUebd-z6IWs",
+        "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e"
+      },
+      "outputs": [],
+      "source": [
+        "import lighteval\n",
+        "import os\n",
+        "from datetime import timedelta\n",
+        "from transformers import AutoModelForCausalLM\n",
+        "\n",
+        "from lighteval.logging.evaluation_tracker import EvaluationTracker\n",
+        "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n",
+        "from lighteval.models.model_config import create_model_config\n",
+        "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n",
+        "\n",
+        "TOKEN = os.getenv(\"HF_TOKEN\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "muikmXNQXgFv"
+      },
+      "outputs": [],
+      "source": [
+        "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n",
+        "\n",
+        "evaluation_tracker = EvaluationTracker(\n",
+        "    output_dir=\"~/tmp\",\n",
+        "    save_details=False,\n",
+        "    push_to_hub=False,\n",
+        "    push_to_tensorboard=False,\n",
+        "    public=False,\n",
+        "    hub_results_org=False,\n",
+        ")\n",
+        "\n",
+        "pipeline_params = PipelineParameters(\n",
+        "    launcher_type=ParallelismManager.ACCELERATE,\n",
+        "    env_config=env_config,\n",
+        "    job_id=1,\n",
+        "    override_batch_size=1,\n",
+        "    num_fewshot_seeds=0,\n",
+        "    max_samples=10,\n",
+        "    use_chat_template=False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nsNjwzCtltkA"
+      },
+      "source": [
+        "# Comparar modelos com `lighteval`\n",
+        "\n",
+        "Vamos comparar dois pequenos LLMs em um domínio. Vamos usar `Qwen2.5-0.5B` e `SmolLM2-360M-Instruct` e vamos avaliá-los no domínio médico.\n",
+        "\n",
+        "Podemos criar uma avaliação de domínio a partir de um subconjunto de avaliações do MMLU, definindo as tarefas de avaliação. No lighteval, as tarefas são descritas como strings.\n",
+        "\n",
+        "`{suite}|{task}:{subtask}|{num_few_shot}|{0 ou 1 para reduzir num_few_shot se o prompt for muito longo}`\n",
+        "\n",
+        "Portanto, passaremos nossa lista de tarefas relacionadas à medicina desta forma:\n",
+        "\n",
+        "```\n",
+        "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
+        "```\n",
+        "\n",
+        "Que pode ser traduzido para:\n",
+        "\n",
+        "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n",
+        "|---|---|---|---|\n",
+        "| leaderboard | mmlu:anatomy | 5 | False |\n",
+        "| leaderboard | mmlu:professional_medicine | 5 | False |\n",
+        "| leaderboard | mmlu:high_school_biology | 5 | False |\n",
+        "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n",
+        "\n",
+        "Para uma lista completa das tarefas suportadas pelo lighteval, confira esta página [na documentação](https://github.com/huggingface/lighteval/wiki/Available-Tasks)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qTqsizv9mVbO"
+      },
+      "outputs": [],
+      "source": [
+        "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XwcJklSFX4H6"
+      },
+      "source": [
+        "# Avaliar Qwen2.5 0.5B"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "cc7ff121f1de4ebfa8f9ce73bfdb962e",
+            "47b6e922e9df42999f5bc7953c44e559",
+            "ffd2517a49d249baa6aac84bc8ed87d2",
+            "1903592fafb3497b87c8bff6c89d22b4",
+            "c2a9eb54064d476abb6000aad8d539c7",
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+            "b89b4b5597e143d5be8db9bb41c50942",
+            "a9eec76a72ce4aefaf12b76268a56a2b",
+            "91ee892ccff34237b6a94da2a2961bc9",
+            "b1bcc93bbe244466a29e165879e3cebc",
+            "86de9c85391040cb96ce799a4d8d80f7",
+            "0c10c0f2c63a407cab9848ce14c74513",
+            "03e247ce7b634467acc9b366cf76d770",
+            "dcb62b29de2c461586b7cbce8e41d50b",
+            "f1ec2ad4ab1f4868961477525f3b0c38",
+            "10f200998209492dadd7a0890de3586e",
+            "95a20101d0364974a9372f6bd9ee567e",
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+        },
+        "id": "PXJuaXVxUNBO",
+        "outputId": "87aca361-5bd5-4b12-9097-3e97f1165d7b"
+      },
+      "outputs": [],
+      "source": [
+        "qwen_model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen2.5-0.5B\")\n",
+        "\n",
+        "pipeline = Pipeline(\n",
+        "    tasks=domain_tasks,\n",
+        "    pipeline_parameters=pipeline_params,\n",
+        "    evaluation_tracker=evaluation_tracker,\n",
+        "    model=qwen_model\n",
+        ")\n",
+        "\n",
+        "pipeline.evaluate()\n",
+        "\n",
+        "qwen_results = pipeline.get_results()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CIwCaCxJX_hA"
+      },
+      "source": [
+        "# Avaliar SmolLM 360M"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+        "id": "Dxg0RtlNVT4y",
+        "outputId": "ee40393c-bf62-499d-d443-e96da72411e8"
+      },
+      "outputs": [],
+      "source": [
+        "smol_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n",
+        "\n",
+        "pipeline = Pipeline(\n",
+        "    tasks=domain_tasks,\n",
+        "    pipeline_parameters=pipeline_params,\n",
+        "    evaluation_tracker=evaluation_tracker,\n",
+        "    model=smol_model\n",
+        ")\n",
+        "\n",
+        "pipeline.evaluate()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pdjyzfKHVt52"
+      },
+      "outputs": [],
+      "source": [
+        "smol_results = pipeline.get_results()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eugvMFfgV1VD",
+        "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96"
+      },
+      "outputs": [],
+      "source": [
+        "pipeline.show_results()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0HD8aFwSYGHu"
+      },
+      "source": [
+        "# Visualizar Resultados\n",
+        "\n",
+        "Agora que temos os resultados dos dois modelos, podemos visualizá-los lado a lado. Manteremos a visualização simples aqui, mas com essa estrutura de dados, você pode representar as pontuações de várias maneiras."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "1sReqrgQUO9r",
+        "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n",
+        "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n",
+        "df = pd.concat([df, _df], axis=1)\n",
+        "df.plot(kind=\"barh\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qJEbQeYDplKX"
+      },
+      "source": [
+        "# 💐 É isso!\n",
+        "\n",
+        "Você agora tem um caderno prático para visualizar as avaliações de modelos. Você pode usá-lo para:\n",
+        "\n",
+        "- Selecionar o modelo certo para o seu caso de uso em inferência\n",
+        "- Avaliar checkpoints durante o treinamento\n",
+        "- Compartilhar pontuações de modelos"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jWdS38syaipm"
+      },
+      "source": [
+        "🏃 Próximos Passos \n",
+        "\n",
+        "- Se você quiser aprofundar seus resultados de avaliação, confira este [caderno](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "A100",
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "widgets": {
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diff --git a/pt-br/4_evaluation/project/README.md b/pt-br/4_evaluation/project/README.md
new file mode 100644
index 00000000..98d32eca
--- /dev/null
+++ b/pt-br/4_evaluation/project/README.md
@@ -0,0 +1,85 @@
+# Domain Specific Evaluation with Argilla, Distilabel, and LightEval
+
+Most popular benchmarks look at very general capabilities (reasoning, math, code), but have you ever needed to study more specific capabilities? 
+
+What should you do if you need to evaluate a model on a **custom domain** relevant to your use-cases? (For example, financial, legal, medical use cases)  
+
+This tutorial shows you the full pipeline you can follow, from creating relevant data and annotating your samples to evaluating your model on them, with the easy to use [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), and [lighteval](https://github.com/huggingface/lighteval). For our example, we'll focus on generating exam questions from multiple documents. 
+
+## Project Structure
+
+For our process, we will follow 4 steps, with a script for each: generating a dataset, annotating it, extracting relevant samples for evaluation from it, and actually evaluating models.
+
+| Script Name | Description |
+|-------------|-------------|
+| generate_dataset.py | Generates exam questions from multiple text documents using a specified language model. |
+| annotate_dataset.py | Creates an Argilla dataset for manual annotation of the generated exam questions. |
+| create_dataset.py | Processes annotated data from Argilla and creates a Hugging Face dataset. |
+| evaluation_task.py | Defines a custom LightEval task for evaluating language models on the exam questions dataset. |
+
+## Steps
+
+### 1. Generate Dataset
+
+The `generate_dataset.py` script uses the distilabel library to generate exam questions based on multiple text documents. It uses the specified model (default: Meta-Llama-3.1-8B-Instruct) to create questions, correct answers, and incrorect answers (known as distractors). You should add you own data samples and you might wish to use a different model.
+
+To run the generation:
+
+```sh
+python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
+```
+
+This will create a [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) containing the generated exam questions for all documents in the input directory. 
+
+### 2. Annotate Dataset
+
+The `annotate_dataset.py` script takes the generated questions and creates an Argilla dataset for annotation. It sets up the dataset structure and populates it with the generated questions and answers, randomizing the order of answers to avoid bias. Once in Argilla, you or a domain expert can validate the dataset with the correct answers.
+
+You will see suggested correct answers from the LLM in random order and you can approve the correct answer or select a different one. The duration of this process will depend on the scale of your evaluation dataset, the complexity of your domain data, and the quality of your LLM. For example, we were able to create 150 samples within 1 hour on the domain of transfer learning, using Llama-3.1-70B-Instruct, mostly by approving the correct answer and discarding the incorrect ones.
+
+To run the annotation process:
+
+```sh
+python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
+```
+
+This will create an Argilla dataset that can be used for manual review and annotation.
+
+![argilla_dataset](./images/domain_eval_argilla_view.png)
+
+If you're not using Argilla, deploy it locally or on spaces following this [quickstart guide](https://docs.argilla.io/latest/getting_started/quickstart/).
+
+### 3. Create Dataset
+
+The `create_dataset.py` script processes the annotated data from Argilla and creates a Hugging Face dataset. It handles both suggested and manually annotated answers. The script will create a dataset with the question, possible answers, and the column name for the correct answer. To create the final dataset:
+
+```sh
+huggingface_hub login
+python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
+```
+
+This will push the dataset to the Hugging Face Hub under the specified repository. You can view the sample dataset on the hub [here](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), and a preview of the dataset looks like this:
+
+![hf_dataset](./images/domain_eval_dataset_viewer.png)
+
+### 4. Evaluation Task
+
+The `evaluation_task.py` script defines a custom LightEval task for evaluating language models on the exam questions dataset. It includes a prompt function, a custom accuracy metric, and the task configuration. 
+
+To evaluate a model using lighteval with the custom exam questions task:
+
+```sh
+lighteval accelerate \
+    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
+    --tasks "community|exam_questions|0|0" \
+    --custom_tasks domain-eval/evaluation_task.py \
+    --output_dir "./evals"
+```
+
+You can find detailed guides in lighteval wiki about each of these steps: 
+
+- [Creating a Custom Task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Creating a Custom Metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Using existing metrics](https://github.com/huggingface/lighteval/wiki/Metric-List)
+
+
diff --git a/pt-br/4_evaluation/project/annotate_dataset.py b/pt-br/4_evaluation/project/annotate_dataset.py
new file mode 100644
index 00000000..dadffc10
--- /dev/null
+++ b/pt-br/4_evaluation/project/annotate_dataset.py
@@ -0,0 +1,129 @@
+import argparse
+import json
+from random import choices, sample
+
+import argilla as rg
+from distilabel.distiset import Distiset
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Annotate exam questions dataset using Argilla."
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="API key for Argilla",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="API URL for Argilla",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Path to the exam questions dataset",
+)
+parser.add_argument(
+    "--dataset_config",
+    type=str,
+    default="default",
+    help="Configuration of the dataset",
+)
+parser.add_argument(
+    "--dataset_split",
+    type=str,
+    default="train",
+    help="Split of the dataset to use",
+)
+parser.add_argument(
+    "--output_dataset_name",
+    type=str,
+    default="exam_questions",
+    help="Name of the output Argilla dataset",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Create Argilla dataset with the feedback task for validation
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+
+if client.datasets(args.output_dataset_name):
+    print(f"Deleting existing dataset '{args.output_dataset_name}'")
+    client.datasets(args.output_dataset_name).delete()
+
+settings = rg.Settings(
+    fields=[
+        rg.TextField("question"),
+        rg.TextField("answer_a"),
+        rg.TextField("answer_b"),
+        rg.TextField("answer_c"),
+        rg.TextField("answer_d"),
+    ],
+    questions=[
+        rg.LabelQuestion(
+            name="correct_answer",
+            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
+        ),
+        rg.TextQuestion(
+            name="improved_question",
+            description="Could you improve the question?",
+        ),
+        rg.TextQuestion(
+            name="improved_answer",
+            description="Could you improve the best answer?",
+        ),
+    ],
+)
+
+dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
+dataset.create()
+
+################################################################################
+# Load the Distiset and process and add records to Argilla dataset
+# We will validate that questions appear in random order to avoid bias
+# but we will show correct answers in the Argilla UI as suggestions.
+################################################################################
+
+distiset = Distiset.load_from_disk(args.dataset_path)
+answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
+dataset_records = []
+
+for exam in distiset[args.dataset_config][args.dataset_split]:
+    exam_json = json.loads(exam["generation"])["exam"]
+
+    for question in exam_json:
+        answer = question["answer"]
+        distractors = question["distractors"]
+        distractors = choices(distractors, k=3)
+        answers = distractors + [answer]
+        answers = sample(answers, len(answers))
+        suggestion_idx = answers.index(answer)
+        fields = dict(zip(answer_names, answers))
+        fields["question"] = question["question"]
+
+        record = rg.Record(
+            fields=fields,
+            suggestions=[
+                rg.Suggestion(
+                    question_name="correct_answer",
+                    value=answer_names[suggestion_idx],
+                )
+            ],
+        )
+        dataset_records.append(record)
+
+dataset.records.log(dataset_records)
+
+print(
+    f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla."
+)
diff --git a/pt-br/4_evaluation/project/create_dataset.py b/pt-br/4_evaluation/project/create_dataset.py
new file mode 100644
index 00000000..822e268e
--- /dev/null
+++ b/pt-br/4_evaluation/project/create_dataset.py
@@ -0,0 +1,72 @@
+import argparse
+
+import argilla as rg
+from datasets import Dataset
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Create a Hugging Face dataset from annotated Argilla data."
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="API key for Argilla",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="API URL for Argilla",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Path to the Argilla dataset",
+)
+parser.add_argument(
+    "--dataset_repo_id",
+    type=str,
+    default="burtenshaw/exam_questions",
+    help="Hugging Face dataset repository ID",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Initialize Argilla client and load dataset
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+dataset = client.datasets(args.dataset_path)
+
+################################################################################
+# Process Argilla records
+################################################################################
+
+dataset_rows = []
+
+for record in dataset.records(with_suggestions=True, with_responses=True):
+    row = record.fields
+
+    if len(record.responses) == 0:
+        answer = record.suggestions["correct_answer"].value
+        row["correct_answer"] = answer
+    else:
+        for response in record.responses:
+            if response.question_name == "correct_answer":
+                row["correct_answer"] = response.value
+    dataset_rows.append(row)
+
+################################################################################
+# Create Hugging Face dataset and push to Hub
+################################################################################
+
+hf_dataset = Dataset.from_list(dataset_rows)
+hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
+
+print(f"Dataset has been successfully pushed to {args.dataset_repo_id}")
diff --git a/pt-br/4_evaluation/project/evaluation_task.py b/pt-br/4_evaluation/project/evaluation_task.py
new file mode 100644
index 00000000..397c75ad
--- /dev/null
+++ b/pt-br/4_evaluation/project/evaluation_task.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+from lighteval.metrics.utils.metric_utils import (
+    SampleLevelMetric,
+    MetricCategory,
+    MetricUseCase,
+)
+
+################################################################################
+# Define the prompt function based on the structure of the dataset
+################################################################################
+
+
+def prompt_fn(line, task_name: str = None):
+    """Converts a dataset line to a Doc object for evaluation."""
+    instruction = "Choose the correct answer for the following exam question:"
+    return Doc(
+        task_name=task_name,
+        query=f"{instruction} {line['question']}",
+        choices=[
+            f" {line['answer_a']}",
+            f" {line['answer_b']}",
+            f" {line['answer_c']}",
+            f" {line['answer_d']}",
+        ],
+        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
+            line["correct_answer"]
+        ),
+        instruction=instruction,
+    )
+
+
+################################################################################
+# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
+# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List
+# Existing metrics can be imported from lighteval.metrics.metrics
+################################################################################
+
+
+def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
+    response = np.argmin(kwargs["choices_logprob"])
+    return response == formatted_doc.gold_index
+
+
+custom_metric = SampleLevelMetric(
+    metric_name="exam_question_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.MULTICHOICE,
+    use_case=MetricUseCase.NONE,
+    sample_level_fn=sample_level_fn,
+    corpus_level_fn=np.mean,
+)
+
+################################################################################
+# Define the task based on the prompt function and the custom metric
+# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
+################################################################################
+
+task = LightevalTaskConfig(
+    name="example",
+    prompt_function=prompt_fn,
+    suite=["community"],
+    hf_repo="burtenshaw/exam_questions",
+    hf_subset="default",
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    few_shots_split=None,
+    few_shots_select=None,
+    metric=[custom_metric],
+)
+
+# Add the task to TASKS_TABLE
+TASKS_TABLE = [task]
+
+# MODULE LOGIC
+if __name__ == "__main__":
+    print([t.name for t in TASKS_TABLE])
+    print(len(TASKS_TABLE))
+
+# lighteval accelerate \
+# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
+# "community|example|0|0" \
+# --custom-tasks "submitted_tasks/example.py" \
+# --output-dir "results"
diff --git a/pt-br/4_evaluation/project/generate_dataset.py b/pt-br/4_evaluation/project/generate_dataset.py
new file mode 100644
index 00000000..cb531d3f
--- /dev/null
+++ b/pt-br/4_evaluation/project/generate_dataset.py
@@ -0,0 +1,167 @@
+import argparse
+import os
+from pydantic import BaseModel, Field
+from datasets import Dataset
+
+from distilabel.llms import InferenceEndpointsLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps.tasks import TextGeneration
+
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Generate exam questions from text files in a directory."
+)
+parser.add_argument(
+    "--model_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="Model ID for text generation",
+)
+parser.add_argument(
+    "--tokenizer_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="Tokenizer ID for text generation",
+)
+parser.add_argument(
+    "--input_dir",
+    type=str,
+    help="Directory containing input text files",
+    default="data",
+)
+parser.add_argument(
+    "--max_new_tokens",
+    type=int,
+    default=2048,
+    help="Maximum number of new tokens to generate",
+)
+parser.add_argument(
+    "--output_path",
+    type=str,
+    default="exam_questions_output",
+    help="Directory to save the generated datasets",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Load the documents
+# We assume that the documents are in the input directory, and that each file
+# is a separate document about the same topic.
+################################################################################
+
+# Process all text files in the input directory
+documents = []
+for filename in os.listdir(args.input_dir):
+    if filename.endswith(".txt"):
+        file_path = os.path.join(args.input_dir, filename)
+        with open(file=file_path, mode="r", encoding="utf-8") as file:
+            document_content = file.read()
+            documents.append(document_content)
+
+# Create a single dataset from all document contents
+dataset = Dataset.from_dict({"document": documents})
+
+################################################################################
+# Define the prompts
+# We use a system prompt to guide the model to generate the correct output format.
+# A template is used to insert the document into the prompt.
+################################################################################
+
+SYSTEM_PROMPT = """\
+You are an exam writer specialized in writing exams for students.
+Your goal is to create questions and answers based on the document provided, 
+and a list of distractors, that are incorrect but viable answers to the question.
+Your answer must adhere to the following format:
+```
+[
+    {
+        "question": "Your question",
+        "answer": "The correct answer to the question",
+        "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"]
+    },
+    ... (more questions and answers as required)
+]
+```
+""".strip()
+
+INSTRUCTION_TEMPLATE = """\
+    Generate a list of answers and questions about the document. 
+    Document:\n\n{{ instruction }}"""
+
+################################################################################
+# Define the output structure
+# We define a data model for the output of the pipeline, this is used to ensure
+# that the output is in the correct format for the evaluation task.
+################################################################################
+
+
+class ExamQuestion(BaseModel):
+    question: str = Field(..., description="The question to be answered")
+    answer: str = Field(..., description="The correct answer to the question")
+    distractors: List[str] = Field(
+        ..., description="A list of incorrect but viable answers to the question"
+    )
+
+
+class ExamQuestions(BaseModel):
+    exam: List[ExamQuestion]
+
+
+################################################################################
+# Create the pipeline
+# We create a pipeline with a single task that generates the exam questions
+# based on the document and in the correct format. We will Hugging Face
+# InferenceEndpoints and the model specified in the arguments.
+################################################################################
+
+with Pipeline(
+    name="Domain-Eval-Questions",
+    description="Generate exam questions based on given documents.",
+) as pipeline:
+    # Set up the text generation task
+    text_generation = TextGeneration(
+        name="exam_generation",
+        llm=InferenceEndpointsLLM(
+            model_id=args.model_id,
+            tokenizer_id=args.model_id,
+            api_key=os.environ["HF_TOKEN"],
+            structured_output={
+                "schema": ExamQuestions.model_json_schema(),
+                "format": "json",
+            },
+        ),
+        input_batch_size=8,
+        output_mappings={"model_name": "generation_model"},
+        input_mappings={"instruction": "document"},
+        system_prompt=SYSTEM_PROMPT,
+        template=INSTRUCTION_TEMPLATE,
+    )
+
+
+################################################################################
+# Run the pipeline
+# We run the pipeline for all documents and save the results to the output path.
+################################################################################
+
+if __name__ == "__main__":
+    # Run the pipeline for all documents
+    distiset = pipeline.run(
+        parameters={
+            "exam_generation": {
+                "llm": {
+                    "generation_kwargs": {
+                        "max_new_tokens": args.max_new_tokens,
+                    }
+                }
+            }
+        },
+        use_cache=False,
+        dataset=dataset,
+    )
+
+    distiset.save_to_disk(args.output_path)
diff --git a/pt-br/4_evaluation/project/images/domain_eval_argilla_view.png b/pt-br/4_evaluation/project/images/domain_eval_argilla_view.png
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diff --git a/pt-br/README.md b/pt-br/README.md
index 386f64fa..ecaf82ac 100644
--- a/pt-br/README.md
+++ b/pt-br/README.md
@@ -2,7 +2,7 @@
 
 # a smol course (um curso miudinho)
 
-Este é um curso prático sobre alinhar modelos de linguagem para o seu caso de uso específico. É uma maneira útil de começar a alinhar modelos de linguagem, porque tudo funciona na maioria das máquinas locais. Existem requisitos mínimos de GPU e nenhum serviço pago. O curso é baseado na série de modelos de [Smollm2] (https://github.com/huggingface/smollm/tree/main), mas você pode transferir as habilidades que aprende aqui para modelos maiores ou outros pequenos modelos de linguagem.
+Este é um curso prático sobre alinhar modelos de linguagem para o seu caso de uso específico. É uma maneira útil de começar a alinhar modelos de linguagem, porque tudo funciona na maioria das máquinas locais. Existem requisitos mínimos de GPU e nenhum serviço pago. O curso é baseado na série de modelos de [SmolLM2](https://github.com/huggingface/smollm/tree/main), mas você pode transferir as habilidades que aprende aqui para modelos maiores ou outros pequenos modelos de linguagem.
 
 <a href="http://hf.co/join/discord">
 <img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
@@ -30,11 +30,12 @@ Este curso fornece uma abordagem prática para trabalhar com pequenos modelos de
 |--------|-------------|---------|--------------|
 | [Instruction Tuning (Ajuste de Instrução)](./1_instruction_tuning) | Aprenda sobre o ajuste fino supervisionado, modelos de bate-papo e a fazer o modelo seguir instruções básicas | ✅ Completo | 3 Dez, 2024 |
 | [Preference Alignment (Alinhamento de Preferência)](./2_preference_alignment) | Explore técnicas DPO e ORPO para alinhar modelos com preferências humanas | ✅ Completo  | 6 Dez, 2024 |
-| [Parameter-efficient Fine-tuning (Ajuste Fino com Eficiência de Parâmetro)](./3_parameter_efficient_finetuning) | Aprenda sobre LoRA, ajuste de prompt e métodos de adaptação eficientes | [🚧 Em Progresso](https://github.com/huggingface/smol-course/pull/41) | 9 Dez, 2024 |
-| [Evaluation (Avaliação)](./4_evaluation) | Use benchmarks automáticos e crie avaliações de domínio personalizadas | [🚧 Em Progresso](https://github.com/huggingface/smol-course/issues/42) | 13 Dez, 2024 |
-| [Vision-language Models (Modelos de Conjunto Visão-linguagem)](./5_vision_language_models) | Adapte modelos multimodais para tarefas visão-linguagem | [🚧 Em Progresso](https://github.com/huggingface/smol-course/issues/49) | 16 Dez, 2024 |
-| [Synthetic Datasets (Conjuntos de Dados Sintéticos)](./6_synthetic_datasets) | Criar e validar conjuntos de dados sintéticos para treinamento | 📝 Planejado | 20 Dez, 2024 |
+| [Parameter-efficient Fine-tuning (Ajuste Fino com Eficiência de Parâmetro)](./3_parameter_efficient_finetuning) | Aprenda sobre LoRA, ajuste de prompt e métodos de adaptação eficientes | ✅ Completo | 9 Dez, 2024 |
+| [Evaluation (Avaliação)](./4_evaluation) | Use benchmarks automáticos e crie avaliações de domínio personalizadas | ✅ Completo | 13 Dez, 2024 |
+| [Vision-language Models (Modelos de Conjunto Visão-linguagem)](./5_vision_language_models) | Adapte modelos multimodais para tarefas visão-linguagem | ✅ Completo | 16 Dez, 2024 |
+| [Synthetic Datasets (Conjuntos de Dados Sintéticos)](./6_synthetic_datasets) | Criar e validar conjuntos de dados sintéticos para treinamento | [🚧 Em Progresso](https://github.com/huggingface/smol-course/issues/83) | 20 Dez, 2024 |
 | [Inference (Inferência)](./7_inference) | Infira modelos com eficiência | 📝 Planejado | 23 Dez, 2024 |
+| Projeto Experimental | Use o que você aprendeu para ser o top 1 na tabela de classificação! | [🚧 Em Progresso](https://github.com/huggingface/smol-course/pull/97) | Dec 23, 2024 |
 
 ## Por Que Pequenos Modelos de Linguagem?
 
@@ -83,7 +84,7 @@ pip install -r requirements.txt
 **A partir do Google Colab** você precisará instalar dependências de maneira flexível com base no hardware que está usando. Pode fazer deste jeito:
 
 ```bash
-pip install -r transformers trl datasets huggingface_hub
+pip install transformers trl datasets huggingface_hub
 ```
 
 ## Engajamento

From c9a9b6ae5ad0c57219aa7ac97b1707a965bc4172 Mon Sep 17 00:00:00 2001
From: ViniFBN <81927867+ViniFBN@users.noreply.github.com>
Date: Fri, 20 Dec 2024 20:35:27 +0100
Subject: [PATCH 07/63] Module 4 - Finished

A file (project\README.md) was not saved properly, now it should be appearing on the repo
---
 pt-br/4_evaluation/project/README.md | 64 ++++++++++++++--------------
 1 file changed, 32 insertions(+), 32 deletions(-)

diff --git a/pt-br/4_evaluation/project/README.md b/pt-br/4_evaluation/project/README.md
index 98d32eca..de40f0b9 100644
--- a/pt-br/4_evaluation/project/README.md
+++ b/pt-br/4_evaluation/project/README.md
@@ -1,72 +1,72 @@
-# Domain Specific Evaluation with Argilla, Distilabel, and LightEval
+# Avaliação Específica de Domínio com Argilla, Distilabel e LightEval
 
-Most popular benchmarks look at very general capabilities (reasoning, math, code), but have you ever needed to study more specific capabilities? 
+Os benchmarks mais populares analisam capacidades muito gerais (raciocínio, matemática, código), mas você já precisou estudar sobre capacidades mais específicas? 
 
-What should you do if you need to evaluate a model on a **custom domain** relevant to your use-cases? (For example, financial, legal, medical use cases)  
+O que fazer se você precisar avaliar um modelo em um **domínio personalizado** relevante para seus casos de uso? (Por exemplo aplicações financeiras, jurídicas ou médicas)
 
-This tutorial shows you the full pipeline you can follow, from creating relevant data and annotating your samples to evaluating your model on them, with the easy to use [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), and [lighteval](https://github.com/huggingface/lighteval). For our example, we'll focus on generating exam questions from multiple documents. 
+Este tutorial mostra todo o pipeline que você pode seguir, desde a criação de dados relevantes e a anotação de suas amostras até a avaliação de seu modelo com elas, usando ferramentas como [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel) e [lighteval](https://github.com/huggingface/lighteval). Para nosso exemplo, focaremos na geração de questões de exame a partir de múltiplos documentos.
 
-## Project Structure
+## Estrutura do Projeto
 
-For our process, we will follow 4 steps, with a script for each: generating a dataset, annotating it, extracting relevant samples for evaluation from it, and actually evaluating models.
+Para o nosso processo, seguiremos 4 etapas, com um script para cada uma: gerar um conjunto de dados, anotar dados, extrair amostras relevantes para avaliação e, finalmente, avaliar os modelos.
 
-| Script Name | Description |
+| Nome do Script | Descrição |
 |-------------|-------------|
-| generate_dataset.py | Generates exam questions from multiple text documents using a specified language model. |
-| annotate_dataset.py | Creates an Argilla dataset for manual annotation of the generated exam questions. |
-| create_dataset.py | Processes annotated data from Argilla and creates a Hugging Face dataset. |
-| evaluation_task.py | Defines a custom LightEval task for evaluating language models on the exam questions dataset. |
+| generate_dataset.py | Gera questões de exame a partir de múltiplos documentos de texto usando um modelo de linguagem especificado.. |
+| annotate_dataset.py | Cria um conjunto de dados no Argilla para anotação manual das questões geradas. |
+| create_dataset.py | Processa os dados anotados no Argilla e cria um conjunto de dados no Hugging Face. |
+| evaluation_task.py | Define uma tarefa personalizada no LightEval para avaliar modelos de linguagem no conjunto de questões de exame. |
 
-## Steps
+## Etapas
 
-### 1. Generate Dataset
+### 1. Gerar Conjunto de Dados
 
-The `generate_dataset.py` script uses the distilabel library to generate exam questions based on multiple text documents. It uses the specified model (default: Meta-Llama-3.1-8B-Instruct) to create questions, correct answers, and incrorect answers (known as distractors). You should add you own data samples and you might wish to use a different model.
+O script `generate_dataset.py` usa o módulo distilabel para gerar questões de exame com base em múltiplos documentos de texto. Ele utiliza o modelo especificado (padrão: Meta-Llama-3.1-8B-Instruct) para criar perguntas, respostas corretas e respostas incorretas (chamadas de distrações). Você deve adicionar seus próprios exemplos de dados e talvez até usar um modelo diferente.
 
-To run the generation:
+Para executar a geração:
 
 ```sh
 python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
 ```
 
-This will create a [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) containing the generated exam questions for all documents in the input directory. 
+Isso criará um [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) contendo as questões de exame geradas para todos os documentos no diretório de entrada.
 
-### 2. Annotate Dataset
+### 2. Anotar Conjunto de Dados
 
-The `annotate_dataset.py` script takes the generated questions and creates an Argilla dataset for annotation. It sets up the dataset structure and populates it with the generated questions and answers, randomizing the order of answers to avoid bias. Once in Argilla, you or a domain expert can validate the dataset with the correct answers.
+O script `annotate_dataset.py` utiliza as questões geradas e cria um conjunto de dados no Argilla para anotação. Ele configura a estrutura do conjunto de dados e o popula com as perguntas e respostas geradas, randomizando a ordem das respostas para evitar vieses (biases). No Argilla, você ou um especialista no domínio pode validar o conjunto de dados com as respostas corretas.
 
-You will see suggested correct answers from the LLM in random order and you can approve the correct answer or select a different one. The duration of this process will depend on the scale of your evaluation dataset, the complexity of your domain data, and the quality of your LLM. For example, we were able to create 150 samples within 1 hour on the domain of transfer learning, using Llama-3.1-70B-Instruct, mostly by approving the correct answer and discarding the incorrect ones.
+Você verá as respostas corretas sugeridas pelo LLM em ordem aleatória e poderá aprovar a resposta correta ou selecionar outra. A duração desse processo dependerá da escala do seu conjunto de avaliação, da complexidade dos dados do domínio e da qualidade do seu LLM. Por exemplo, fomos capazes de criar 150 amostras em 1 hora no domínio de transferência de aprendizado, usando o Llama-3.1-70B-Instruct, aprovando principalmente as respostas corretas e descartando as incorretas
 
-To run the annotation process:
+Para executar o processo de anotação:
 
 ```sh
 python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
 ```
 
-This will create an Argilla dataset that can be used for manual review and annotation.
+Isso criará um conjunto de dados no Argilla que pode ser usado para revisão e anotação manual.
 
 ![argilla_dataset](./images/domain_eval_argilla_view.png)
 
-If you're not using Argilla, deploy it locally or on spaces following this [quickstart guide](https://docs.argilla.io/latest/getting_started/quickstart/).
+Se você não estiver usando o Argilla, implante-o localmente ou no Spaces seguindo este [guia de início rápido](https://docs.argilla.io/latest/getting_started/quickstart/).
 
-### 3. Create Dataset
+### 3. Criar Conjunto de Dados
 
-The `create_dataset.py` script processes the annotated data from Argilla and creates a Hugging Face dataset. It handles both suggested and manually annotated answers. The script will create a dataset with the question, possible answers, and the column name for the correct answer. To create the final dataset:
+O script `create_dataset.py` processa os dados anotados no Argilla e cria um conjunto de dados no Hugging Face. Ele manipula tanto as respostas sugeridas quanto as anotadas manualmente. O script criará um conjunto de dados contendo a pergunta, as possíveis respostas e o nome da coluna para a resposta correta. Para criar o conjunto de dados final:
 
 ```sh
 huggingface_hub login
 python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
 ```
 
-This will push the dataset to the Hugging Face Hub under the specified repository. You can view the sample dataset on the hub [here](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), and a preview of the dataset looks like this:
+Isso enviará o conjunto de dados para o Hugging Face Hub sob o repositório especificado. Você pode visualizar o conjunto de dados de exemplo no hub [aqui](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), e uma pré-visualização do conjunto de dados se parece com isto:
 
 ![hf_dataset](./images/domain_eval_dataset_viewer.png)
 
-### 4. Evaluation Task
+### 4. Tarefa de Avaliação
 
-The `evaluation_task.py` script defines a custom LightEval task for evaluating language models on the exam questions dataset. It includes a prompt function, a custom accuracy metric, and the task configuration. 
+O script `evaluation_task.py` define uma tarefa personalizada no LightEval para avaliar modelos de linguagem no conjunto de questões de exame. Ele inclui uma função de prompt, uma métrica de precisão personalizada e a configuração da tarefa. 
 
-To evaluate a model using lighteval with the custom exam questions task:
+Para avaliar um modelo usando o lighteval com a tarefa personalizada de questões de exame:
 
 ```sh
 lighteval accelerate \
@@ -76,10 +76,10 @@ lighteval accelerate \
     --output_dir "./evals"
 ```
 
-You can find detailed guides in lighteval wiki about each of these steps: 
+Você pode encontrar guias detalhados no wiki do lighteval sobre cada uma dessas etapas: 
 
-- [Creating a Custom Task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Creating a Custom Metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Using existing metrics](https://github.com/huggingface/lighteval/wiki/Metric-List)
+- [Criando uma Tarefa Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Criando uma Métrica Personalizada](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Usando Métricas Existentes](https://github.com/huggingface/lighteval/wiki/Metric-List)
 
 

From bf6de637f1a552cdc06758619d624c3afed74118 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Fri, 20 Dec 2024 22:14:47 +0100
Subject: [PATCH 08/63] init

---
 vi/3_parameter_efficient_finetuning/README.md |   38 +
 .../images/lora_adapter.png                   |  Bin 0 -> 1239572 bytes
 .../images/prompt_tuning.png                  |  Bin 0 -> 85192 bytes
 .../lora_adapters.md                          |  124 +
 .../notebooks/finetune_sft_peft.ipynb         |  510 ++
 .../notebooks/load_lora_adapter.ipynb         | 5059 +++++++++++++++++
 .../prompt_tuning.md                          |   76 +
 vi/README.md                                  |    4 +-
 8 files changed, 5809 insertions(+), 2 deletions(-)
 create mode 100644 vi/3_parameter_efficient_finetuning/README.md
 create mode 100644 vi/3_parameter_efficient_finetuning/images/lora_adapter.png
 create mode 100644 vi/3_parameter_efficient_finetuning/images/prompt_tuning.png
 create mode 100644 vi/3_parameter_efficient_finetuning/lora_adapters.md
 create mode 100644 vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
 create mode 100644 vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
 create mode 100644 vi/3_parameter_efficient_finetuning/prompt_tuning.md

diff --git a/vi/3_parameter_efficient_finetuning/README.md b/vi/3_parameter_efficient_finetuning/README.md
new file mode 100644
index 00000000..207aa18d
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/README.md
@@ -0,0 +1,38 @@
+# Tinh chỉnh hiệu quả tham số (Parameter-Efficient Fine-Tuning - PEFT)
+
+Khi các mô hình ngôn ngữ ngày càng lớn hơn, việc tinh chỉnh truyền thống trở nên ngày càng thách thức. Việc tinh chỉnh đầy đủ một mô hình với 1.7B tham số đòi hỏi bộ nhớ GPU lớn, việc lưu trữ các bản sao mô hình riêng biệt tốn kém, và có nguy cơ làm mất đi các khả năng ban đầu của mô hình. Các phương pháp tinh chỉnh hiệu quả tham số (PEFT) giải quyết những thách thức này bằng cách chỉ điều chỉnh một tập nhỏ các tham số mô hình trong khi giữ nguyên phần lớn mô hình.
+
+Tinh chỉnh truyền thống cập nhật tất cả các tham số mô hình trong quá trình huấn luyện, điều này trở nên không khả thi với các mô hình lớn. Các phương pháp PEFT giới thiệu cách tiếp cận để điều chỉnh mô hình sử dụng ít tham số có thể huấn luyện hơn - thường ít hơn 1% kích thước mô hình gốc. Việc giảm đáng kể số lượng tham số có thể huấn luyện cho phép:
+
+- Tinh chỉnh trên phần cứng tiêu dùng với bộ nhớ GPU hạn chế 
+- Lưu trữ nhiều phiên bản điều chỉnh (adapters) cho từng tác vụ một cách hiệu quả
+- Khả năng tổng quát hóa tốt hơn trong các trường hợp dữ liệu ít
+- Chu kỳ huấn luyện và thử nghiệm nhanh hơn
+
+## Các phương pháp hiện có
+
+Trong chương này, chúng ta sẽ tìm hiểu hai phương pháp PEFT phổ biến:
+
+### 1️⃣ Phương Pháp LoRA (Low-Rank Adaptation)
+
+LoRA đã nổi lên như phương pháp PEFT được áp dụng rộng rãi nhất, cung cấp giải pháp hoàn hảo cho việc điều chỉnh mô hình hiệu quả mà không tốn nhiều tài nguyên tính toán. Thay vì sửa đổi toàn bộ mô hình, **LoRA đưa các ma trận có thể huấn luyện vào các lớp attention của mô hình.** Cách tiếp cận này thường giảm các tham số có thể huấn luyện khoảng 90% trong khi vẫn duy trì hiệu suất tương đương với tinh chỉnh đầy đủ. Chúng ta sẽ khám phá LoRA trong phần [LoRA (Low-Rank Adaptation)](./lora_adapters.md).
+
+### 2️⃣ Phương Pháp Điều Chỉnh Chỉ Thị (Prompt Tuning)
+
+Prompt tuning cung cấp cách tiếp cận **thậm chí nhẹ hơn** bằng cách **thêm các token có thể huấn luyện vào đầu vào** thay vì sửa đổi trọng số mô hình. Prompt tuning ít phổ biến hơn LoRA, nhưng có thể là kỹ thuật hữu ích để nhanh chóng điều chỉnh mô hình cho các tác vụ hoặc lĩnh vực mới. Chúng ta sẽ khám phá prompt tuning trong phần [Prompt Tuning](./prompt_tuning.md).
+
+## Notebooks bài tập
+
+| Tiêu đề | Mô tả | Bài tập | Link | Colab |
+|---------|--------|---------|------|-------|
+| Tinh chỉnh LoRA | Học cách tinh chỉnh mô hình sử dụng LoRA adapters | 🐢 Huấn luyện mô hình sử dụng LoRA<br>🐕 Thử nghiệm với các giá trị rank khác nhau<br>🦁 So sánh hiệu suất với tinh chỉnh đầy đủ | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| Tải LoRA Adapters | Học cách tải và sử dụng LoRA adapters đã huấn luyện | 🐢 Tải adapters đã huấn luyện trước<br>🐕 Gộp adapters với mô hình cơ sở<br>🦁 Chuyển đổi giữa nhiều adapters | [Notebook](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## Tài nguyên
+- [Tài liệu PEFT](https://huggingface.co/docs/peft)
+- [Bài báo nghiên cứu LoRA](https://arxiv.org/abs/2106.09685)
+- [Bài báo nghiên cứu QLoRA](https://arxiv.org/abs/2305.14314)
+- [Bài báo nghiên cứu Prompt Tuning](https://arxiv.org/abs/2104.08691)
+- [Hướng dẫn sử dụng PEFT của Hugging Face](https://huggingface.co/blog/peft)
+- [Cách Tinh chỉnh LLM với Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl)
+- [Thư viện TRL](https://huggingface.co/docs/trl/index)
\ No newline at end of file
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diff --git a/vi/3_parameter_efficient_finetuning/lora_adapters.md b/vi/3_parameter_efficient_finetuning/lora_adapters.md
new file mode 100644
index 00000000..449dc2b9
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/lora_adapters.md
@@ -0,0 +1,124 @@
+# Phương Pháp LoRA (Low-Rank Adaptation)
+
+LoRA đã trở thành phương pháp PEFT được sử dụng rộng rãi nhất. Nó hoạt động bằng cách thêm các ma trận phân rã hạng thấp (small rank decomposition matrices) vào các trọng số attention, điều này dẫn đến việc giảm khoảng 90% số lượng tham số có thể huấn luyện.
+
+## Tìm Hiểu Về LoRA
+
+LoRA (Low-Rank Adaptation) là một kỹ thuật tinh chỉnh hiệu quả tham số, đóng băng các trọng số của mô hình đã huấn luyện trước và đưa thêm các ma trận phân rã hạng (rank decomposition matrices) có thể huấn luyện vào các lớp của mô hình. Thay vì huấn luyện tất cả các tham số mô hình trong quá trình tinh chỉnh, LoRA phân rã việc cập nhật trọng số thành các ma trận nhỏ hơn thông qua phân rã hạng thấp (low-rank decomposition), giảm đáng kể số lượng tham số có thể huấn luyện trong khi vẫn duy trì hiệu suất mô hình. Ví dụ, khi áp dụng cho GPT-3 175B, LoRA giảm số lượng tham số có thể huấn luyện xuống 10.000 lần và yêu cầu bộ nhớ GPU giảm 3 lần so với tinh chỉnh đầy đủ. Bạn có thể đọc thêm về LoRA trong [bài báo nghiên cứu LoRA](https://arxiv.org/pdf/2106.09685).
+
+LoRA hoạt động bằng cách thêm các cặp ma trận phân rã hạng vào các lớp transformer, thường tập trung vào các trọng số attention. Trong quá trình suy luận, các trọng số adapter có thể được gộp với mô hình cơ sở, không gây thêm độ trễ. LoRA đặc biệt hữu ích cho việc điều chỉnh các mô hình ngôn ngữ lớn cho các tác vụ hoặc lĩnh vực cụ thể trong khi không yêu cầu nhiều tài nguyên tính toán.
+
+## Lắp Các LoRA Adapters Vào Mô Hình Ngôn Ngữ
+
+Adapters có thể được lắp vào một mô hình đã huấn luyện trước với hàm `load_adapter()`, điều này hữu ích khi thử nghiệm các adapters khác nhau mà trọng số không được gộp. Đặt trọng số adapter đang hoạt động bằng hàm `set_adapter()`. Để trở về mô hình cơ sở, bạn có thể sử dụng `unload()` để gỡ tất cả các module LoRA. Điều này giúp dễ dàng chuyển đổi giữa các trọng số cho từng tác vụ cụ thể.
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
+peft_model_id = "<peft_adapter_id>"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+```
+
+![lora_load_adapter](./images/lora_adapter.png)
+
+## Gộp LoRA Adapters
+
+Sau khi huấn luyện với LoRA, bạn có thể muốn gộp các trọng số adapter trở lại mô hình cơ sở để triển khai dễ dàng hơn. Điều này tạo ra một mô hình duy nhất với các trọng số đã kết hợp, loại bỏ nhu cầu tải adapters riêng biệt trong quá trình suy luận.
+
+Quá trình gộp đòi hỏi chú ý đến quản lý bộ nhớ và độ chính xác. Vì bạn sẽ cần tải cả mô hình cơ sở và trọng số adapter cùng lúc, hãy đảm bảo có đủ bộ nhớ GPU/CPU. Sử dụng `device_map="auto"` trong `transformers` sẽ giúp tự động quản lý bộ nhớ. Duy trì độ chính xác nhất quán (ví dụ: float16) trong suốt quá trình, phù hợp với độ chính xác được sử dụng trong quá trình huấn luyện và lưu mô hình đã gộp trong cùng một định dạng để triển khai. Trước khi triển khai, luôn xác thực mô hình đã gộp bằng cách so sánh đầu ra và các chỉ số hiệu suất với phiên bản dựa trên adapter.
+
+Adapters cũng thuận tiện cho việc chuyển đổi giữa các tác vụ hoặc lĩnh vực khác nhau. Bạn có thể tải mô hình cơ sở và trọng số adapter riêng biệt. Điều này cho phép chuyển đổi nhanh chóng giữa các trọng số cho từng tác vụ cụ thể.
+
+## Hướng dẫn triển khai
+
+Thư mục `notebooks/` chứa các hướng dẫn thực hành và bài tập để triển khai các phương pháp PEFT khác nhau. Bắt đầu với `load_lora_adapter_example.ipynb` để có giới thiệu cơ bản, sau đó khám phá `lora_finetuning.ipynb` để xem xét chi tiết hơn về cách tinh chỉnh một mô hình với LoRA và SFT.
+
+Khi triển khai các phương pháp PEFT, hãy bắt đầu với các giá trị hạng nhỏ (4-8) cho LoRA và theo dõi mất mát trong quá trình huấn luyện. Sử dụng tập validation để tránh overfitting và so sánh kết quả với các baseline tinh chỉnh đầy đủ khi có thể. Hiệu quả của các phương pháp khác nhau có thể thay đổi theo tác vụ, vì vậy thử nghiệm là chìa khóa.
+
+## OLoRA
+
+[OLoRA](https://arxiv.org/abs/2406.01775) sử dụng phân rã QR để khởi tạo các adapter LoRA. OLoRA dịch chuyển các trọng số cơ sở của mô hình bằng một hệ số của phân rã QR của chúng, tức là nó thay đổi trọng số trước khi thực hiện bất kỳ huấn luyện nào trên chúng. Cách tiếp cận này cải thiện đáng kể tính ổn định, tăng tốc độ hội tụ và cuối cùng đạt được hiệu suất vượt trội.
+
+## Sử dụng TRL với PEFT
+
+Các phương pháp PEFT có thể được kết hợp với thư viện TRL để tinh chỉnh hiệu quả. Sự tích hợp này đặc biệt hữu ích cho RLHF (Reinforcement Learning from Human Feedback) vì nó giảm yêu cầu bộ nhớ.
+
+```python
+from peft import LoraConfig
+from transformers import AutoModelForCausalLM
+
+# Tải mô hình với cấu hình PEFT
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# Tải mô hình trên thiết bị cụ thể
+model = AutoModelForCausalLM.from_pretrained(
+    "your-model-name",
+    load_in_8bit=True,  # Tùy chọn: sử dụng độ chính xác 8-bit
+    device_map="auto",
+    peft_config=lora_config
+)
+```
+
+Ở trên, chúng ta đã sử dụng `device_map="auto"` để tự động gán mô hình cho thiết bị phù hợp. Bạn cũng có thể gán thủ công mô hình cho một thiết bị cụ thể bằng cách sử dụng `device_map={"": device_index}`. Bạn cũng có thể mở rộng việc huấn luyện trên nhiều GPU trong khi vẫn giữ việc sử dụng bộ nhớ hiệu quả.
+
+## Triển khai gộp cơ bản
+
+Sau khi huấn luyện một adapter LoRA, bạn có thể gộp trọng số adapter trở lại mô hình cơ sở. Đây là cách thực hiện:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+# 1. Tải mô hình cơ sở
+base_model = AutoModelForCausalLM.from_pretrained(
+    "base_model_name",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# 2. Tải LoRA adapter
+peft_model = PeftModel.from_pretrained(
+    base_model,
+    "path/to/adapter",
+    torch_dtype=torch.float16
+)
+
+# 3. Gộp trọng số adapter với mô hình cơ sở
+try:
+    merged_model = peft_model.merge_and_unload()
+except RuntimeError as e:
+    print(f"Gộp thất bại: {e}")
+    # Triển khai chiến lược dự phòng hoặc tối ưu hóa bộ nhớ
+
+# 4. Lưu mô hình đã gộp
+merged_model.save_pretrained("path/to/save/merged_model")
+```
+
+Nếu bạn gặp sự khác biệt về kích thước trong mô hình đã lưu, hãy đảm bảo bạn cũng lưu tokenizer:
+
+```python
+# Lưu cả mô hình và tokenizer
+tokenizer = AutoTokenizer.from_pretrained("base_model_name")
+merged_model.save_pretrained("path/to/save/merged_model")
+tokenizer.save_pretrained("path/to/save/merged_model")
+```
+
+## Các bước tiếp theo
+
+⏩ Chuyển sang [Hướng dẫn Prompt Tuning](prompt_tuning.md) để tìm hiểu cách tinh chỉnh một mô hình bằng prompt tuning.
+⏩ Chuyển sang [Hướng dẫn lắp LoRA Adapters](./notebooks/load_lora_adapter.ipynb) để tìm hiểu cách lắp LoRA adapters.
+
+# Tài nguyên
+
+- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
+- [Tài liệu PEFT](https://huggingface.co/docs/peft)
+- [Bài viết blog của Hugging Face về PEFT](https://huggingface.co/blog/peft)
\ No newline at end of file
diff --git a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
new file mode 100644
index 00000000..f53c614a
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -0,0 +1,510 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z-6LLOPZouLg"
+   },
+   "source": [
+    "# Cách tinh chỉnh LLM với LoRA Adapters sử dụng Hugging Face TRL\n",
+    "\n",
+    "Trong Notebook này bạn sẽ được học cách tinh chỉnh hiệu quả các mô hình ngôn ngữ lớn sử dụng LoRA (Low-Rank Adaptation) adapters. LoRA là một kỹ thuật tinh chỉnh tham số hiệu quả:\n",
+    "- Đóng băng các trọng số mô hình đã huấn luyện trước\n",
+    "- Thêm các ma trận phân rã hạng nhỏ có thể huấn luyện vào các lớp attention  \n",
+    "- Thường giảm khoảng 90% tham số có thể huấn luyện\n",
+    "- Duy trì hiệu suất mô hình trong khi sử dụng bộ nhớ hiệu quả\n",
+    "\n",
+    "Chúng ta sẽ tìm hiểu:  \n",
+    "1. Cài đặt môi trường phát triển và cấu hình LoRA\n",
+    "2. Tạo và chuẩn bị dữ liệu để huấn luyện adapter \n",
+    "3. Tinh chỉnh sử dụng `trl` và `SFTTrainer` với LoRA adapters\n",
+    "4. Kiểm tra mô hình và gộp adapters (tùy chọn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fXqd9BXgouLi"
+   },
+   "source": [
+    "## 1. Cài đặt môi trường phát triển\n",
+    "\n",
+    "Bước đầu tiên là cài đặt các thư viện Hugging Face và PyTorch, bao gồm `trl, transformers và datasets`. Nếu bạn chưa nghe nói về `trl`, đừng lo lắng. Đó là một thư viện mới trên nền tảng transformers và datasets, giúp việc tinh chỉnh các mô hình ngôn ngữ lớn (LLM) trở nên dễ dàng hơn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tKvGVxImouLi"
+   },
+   "outputs": [],
+   "source": [
+    "# Cài đặt các yêu cầu trong Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Xác thực với Hugging Face\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# để thuận tiện bạn có thể tạo một biến môi trường chứa token hub của bạn là HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XHUzfwpKouLk"
+   },
+   "source": [
+    "## 2. Tải dữ liệu "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 2260\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 119\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Tải một tập dữ liệu mẫu\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: xác định dataset và config của bạn sử dụng các tham số path và name\n",
+    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TOhJdtsouLk"
+   },
+   "source": [
+    "## 3. Tinh chỉnh LLM sử dụng `trl` và `SFTTrainer` với LoRA\n",
+    "\n",
+    "[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) từ `trl` cung cấp tích hợp với LoRA adapters thông qua thư viện [PEFT](https://huggingface.co/docs/peft/en/index). Những lợi thế chính của cài đặt này bao gồm:\n",
+    "\n",
+    "1. **Hiệu quả bộ nhớ**:\n",
+    "   - Chỉ các tham số adapter được lưu trữ trong bộ nhớ GPU \n",
+    "   - Trọng số mô hình cơ sở vẫn đóng băng và có thể được tải với độ chính xác thấp hơn\n",
+    "   - Cho phép tinh chỉnh các mô hình lớn trên GPU tiêu dùng\n",
+    "\n",
+    "2. **Tính năng huấn luyện**:\n",
+    "   - Tích hợp PEFT/LoRA sẵn có với cài đặt tối thiểu\n",
+    "   - Hỗ trợ QLoRA (LoRA lượng tử hóa) cho hiệu quả bộ nhớ tốt hơn\n",
+    "\n",
+    "3. **Quản lý Adapter**: \n",
+    "   - Lưu trọng số adapter trong quá trình checkpoint\n",
+    "   - Tính năng gộp adapters trở lại mô hình cơ sở\n",
+    "\n",
+    "Chúng ta sẽ sử dụng LoRA trong ví dụ của mình, **kết hợp LoRA với lượng tử hóa 4-bit** để giảm thêm việc sử dụng bộ nhớ mà không ảnh hưởng đến hiệu suất. Cài đặt chỉ yêu cầu một vài bước cấu hình:\n",
+    "1. Xác định cấu hình LoRA (rank, alpha, dropout)\n",
+    "2. Tạo SFTTrainer với cấu hình PEFT \n",
+    "3. Huấn luyện và lưu trọng số adapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Các thư viện cần thiết \n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Tải mô hình và tokenizer\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# Thiết lập định dạng chat\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Đặt tên cho bản tinh chỉnh để lưu &/ tải lên\n",
+    "finetune_name = \"SmolLM2-FT-LoRA-Adapter\"\n",
+    "finetune_tags = [\"smol-course\", \"module_3\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZbuVArTHouLk"
+   },
+   "source": [
+    "`SFTTrainer` hỗ trợ tích hợp sẵn với `peft`, điều này giúp tinh chỉnh hiệu quả các LLM dễ dàng hơn bằng cách sử dụng, ví dụ như LoRA. Chúng ta chỉ cần tạo `LoraConfig` và cung cấp nó cho trainer.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Bài tập: Xác định tham số LoRA cho tinh chỉnh</h2>\n",
+    "    <p>Lấy một bộ dữ liệu từ Hugging Face hub và tinh chỉnh một mô hình trên nó.</p>\n",
+    "    <p><b>Mức độ khó</b></p> \n",
+    "    <p>🐢 Sử dụng các tham số chung cho một bản tinh chỉnh tùy ý</p>\n",
+    "    <p>🐕 Điều chỉnh các tham số và so sánh trong weights & biases.</p>\n",
+    "    <p>🦁 Điều chỉnh các tham số và cho thấy sự thay đổi trong kết quả suy luận.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "blDSs9swouLk"
+   },
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig\n",
+    "\n",
+    "# TODO: Điều chỉnh cấu hình tham số LoRA\n",
+    "# r: chiều rank cho ma trận cập nhật LoRA (nhỏ hơn = nén nhiều hơn)\n",
+    "rank_dimension = 4\n",
+    "# lora_alpha: hệ số tỷ lệ cho các lớp LoRA (cao hơn = điều chỉnh mạnh hơn) \n",
+    "lora_alpha = 8\n",
+    "# lora_dropout: xác suất dropout cho các lớp LoRA (giúp tránh overfitting)\n",
+    "lora_dropout = 0.05\n",
+    "\n",
+    "peft_config = LoraConfig(\n",
+    "    r=rank_dimension,  # Chiều rank - thường từ 4-32\n",
+    "    lora_alpha=lora_alpha,  # Hệ số tỷ lệ LoRA - thường gấp 2 lần rank\n",
+    "    lora_dropout=lora_dropout,  # Xác suất dropout cho các lớp LoRA \n",
+    "    bias=\"none\",  # Loại bias cho LoRA. các bias tương ứng sẽ được cập nhật trong quá trình huấn luyện.\n",
+    "    target_modules=\"all-linear\",  # Những module nào áp dụng LoRA\n",
+    "    task_type=\"CAUSAL_LM\",  # Loại tác vụ cho kiến trúc mô hình\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l5NUDPcaouLl"
+   },
+   "source": [
+    "Trước khi bắt đầu huấn luyện, chúng ta cần xác định các `siêu tham số (TrainingArguments)` mà chúng ta muốn sử dụng."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NqT28VZlouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# Cấu hình huấn luyện  \n",
+    "# Siêu tham số dựa trên khuyến nghị từ bài báo QLoRA \n",
+    "args = SFTConfig(\n",
+    "    # Cài đặt đầu ra\n",
+    "    output_dir=finetune_name,  # Thư mục để lưu checkpoint mô hình\n",
+    "    # Thời gian huấn luyện\n",
+    "    num_train_epochs=1,  # Số epoch huấn luyện\n",
+    "    # Cài đặt kích thước batch \n",
+    "    per_device_train_batch_size=2,  # Kích thước batch cho mỗi GPU\n",
+    "    gradient_accumulation_steps=2,  # Tích lũy gradient cho batch hiệu quả lớn hơn\n",
+    "    # Tối ưu bộ nhớ\n",
+    "    gradient_checkpointing=True,  # Đánh đổi tính toán để tiết kiệm bộ nhớ\n",
+    "    # Cài đặt optimizer\n",
+    "    optim=\"adamw_torch_fused\",  # Sử dụng AdamW fusion cho hiệu quả\n",
+    "    learning_rate=2e-4,  # Tốc độ học (từ bài báo QLoRA)\n",
+    "    max_grad_norm=0.3,  # Ngưỡng cắt gradient\n",
+    "    # Lịch trình học\n",
+    "    warmup_ratio=0.03,  # Phần bước cho warmup\n",
+    "    lr_scheduler_type=\"constant\",  # Giữ tốc độ học không đổi sau warmup\n",
+    "    # Ghi log và lưu\n",
+    "    logging_steps=10,  # Ghi metrics mỗi N bước\n",
+    "    save_strategy=\"epoch\",  # Lưu checkpoint mỗi epoch\n",
+    "    # Cài đặt độ chính xác\n",
+    "    bf16=True,  # Sử dụng độ chính xác bfloat16\n",
+    "    # Cài đặt tích hợp\n",
+    "    push_to_hub=False,  # Không đẩy lên HuggingFace Hub\n",
+    "    report_to=None,  # Tắt ghi log bên ngoài\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cGhR7uFBouLl"
+   },
+   "source": [
+    "Bây giờ chúng ta đã có mọi thứ cần thiết để tạo `SFTTrainer` và bắt đầu huấn luyện mô hình của mình."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "M00Har2douLl"
+   },
+   "outputs": [],
+   "source": [
+    "max_seq_length = 1512  # độ dài chuỗi tối đa cho mô hình và đóng gói (packing) bộ dữ liệu\n",
+    "\n",
+    "# Tạo SFTTrainer với cấu hình LoRA\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    peft_config=peft_config,  # Cấu hình LoRA\n",
+    "    max_seq_length=max_seq_length,  # Độ dài chuỗi tối đa \n",
+    "    tokenizer=tokenizer,\n",
+    "    packing=True,  # Bật đóng gói đầu vào cho hiệu quả \n",
+    "    dataset_kwargs={\n",
+    "        \"add_special_tokens\": False,  # Token đặc biệt được xử lý bởi template\n",
+    "        \"append_concat_token\": False,  # Không cần thêm separator \n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zQ_kRN24ouLl"
+   },
+   "source": [
+    "Bắt đầu huấn luyện mô hình bằng cách gọi phương thức `train()` trên `Trainer` của chúng ta. Việc này sẽ bắt đầu vòng lặp huấn luyện và huấn luyện mô hình của chúng ta trong `3 epochs`. Vì chúng ta đang sử dụng phương pháp PEFT, chúng ta sẽ chỉ lưu phần trọng số của adapter đã điều chỉnh và không lưu toàn bộ mô hình."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Tq4nIYqKouLl"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "300e5dfbb4b54750b77324345c7591f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/72 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# bắt đầu huấn luyện, mô hình sẽ tự động được lưu lên hub và thư mục đầu ra\n",
+    "trainer.train()\n",
+    "\n",
+    "# lưu mô hình\n",
+    "trainer.save_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y4HHSYYzouLl"
+   },
+   "source": [
+    "Việc huấn luyện với Flash Attention cho 3 epoch với một dataset 15k mẫu mất `4:14:36` trên một cụm máy `g5.2xlarge` của AWS. Cụm máy này có giá `1.21$/h` và tổng chi phí của lần huấn luyện này chỉ tốn khoảng `5.3$`.\n",
+    "\n",
+    "**Ghi chú: bạn hoàn toàn có thể sử dụng GPU của Kaggle hoặc Google Colab để huấn luyện**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C309KsXjouLl"
+   },
+   "source": [
+    "### Gộp LoRA Adapter vào Mô hình Gốc\n",
+    "\n",
+    "Khi sử dụng LoRA, chúng ta chỉ huấn luyện trọng số adapter trong khi giữ nguyên mô hình cơ sở. Trong quá trình huấn luyện, chúng ta chỉ lưu những trọng số adapter nhẹ này (~2-10MB) thay vì một bản sao mô hình đầy đủ. Tuy nhiên, để triển khai, bạn có thể muốn gộp các adapter trở lại mô hình cơ sở để:\n",
+    "\n",
+    "1. **Đơn giản hóa triển khai**: Một file mô hình thay vì mô hình cơ sở + adapters\n",
+    "2. **Tốc độ suy luận**: Không có chi phí tính toán adapter phụ thêm\n",
+    "3. **Tương thích Framework**: Tương thích tốt hơn với các framework phục vụ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import AutoPeftModelForCausalLM\n",
+    "\n",
+    "\n",
+    "# Tải mô hình PEFT trên CPU  \n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=args.output_dir,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    low_cpu_mem_usage=True,\n",
+    ")\n",
+    "\n",
+    "# Gộp LoRA và mô hình cơ sở và lưu\n",
+    "merged_model = model.merge_and_unload()\n",
+    "merged_model.save_pretrained(\n",
+    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yO6E9quouLl"
+   },
+   "source": [
+    "## 3. Kiểm tra Mô hình\n",
+    "\n",
+    "Sau khi huấn luyện hoàn tất, chúng ta muốn kiểm tra mô hình của mình. Chúng ta sẽ tải các mẫu khác nhau từ dataset gốc và đánh giá mô hình trên những mẫu đó, sử dụng một vòng lặp đơn giản và độ chính xác làm điểm số đánh giá.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Bài tập Bonus: Tải LoRA Adapter</h2>\n",
+    "    <p>Sử dụng những gì bạn đã học được từ notebook ví dụ để tải adapter LoRA đã huấn luyện của bạn cho suy luận.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "I5B494OdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# giải phóng bộ nhớ một lần nữa\n",
+    "del model\n",
+    "del trainer\n",
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "P1UhohVdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from peft import AutoPeftModelForCausalLM \n",
+    "from transformers import AutoTokenizer, pipeline\n",
+    "\n",
+    "# Tải Mô hình với PEFT adapter\n",
+    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
+    ")\n",
+    "pipe = pipeline(\n",
+    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "99uFDAuuouLl"
+   },
+   "source": [
+    "Hãy thử một số chỉ định mẫu và xem mô hình hoạt động như thế nào."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "-shSmUbvouLl",
+    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
+   },
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"Thủ đô của Việt Nam thành phố nào? Giải thích tại sao là như vậy và liệu nó có khác trong quá khứ không?\",\n",
+    "    \"Viết một hàm Python để tính giai thừa của một số.\",\n",
+    "    \"Một khu vườn hình chữ nhật có chiều dài 25 mét và chiều rộng 15 mét. Nếu bạn muốn xây một hàng rào xung quanh toàn bộ khu vườn, bạn sẽ cần bao nhiêu mét hàng rào?\",\n",
+    "    \"Sự khác biệt giữa trái cây và rau củ là gì? Đưa ra ví dụ cho mỗi loại.\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def test_inference(prompt):\n",
+    "    prompt = pipe.tokenizer.apply_chat_template(\n",
+    "        [{\"role\": \"user\", \"content\": prompt}],\n",
+    "        tokenize=False,\n",
+    "        add_generation_prompt=True,\n",
+    "    )\n",
+    "    outputs = pipe(\n",
+    "        prompt,\n",
+    "    )\n",
+    "    return outputs[0][\"generated_text\"][len(prompt):].strip()\n",
+    "\n",
+    "\n",
+    "for prompt in prompts:\n",
+    "    print(f\"    prompt:\\n{prompt}\")\n",
+    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
+    "    print(\"-\" * 50)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
new file mode 100644
index 00000000..73d258e9
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
@@ -0,0 +1,5059 @@
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+      "outputs": [],
+      "source": [
+        "!pip install -q bitsandbytes datasets accelerate loralib\n",
+        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
+      ]
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+        "id": "cg3fiQOvmI3Q",
+        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import bitsandbytes as bnb\n",
+        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S65GcxNGA9kz"
+      },
+      "source": [
+        "## Sử dụng adapters từ Hugging Face Hub"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517,
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+        "id": "hsD1VKqeA62Z",
+        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import transformers\n",
+        "from datasets import load_dataset\n",
+        "from peft import LoraConfig, get_peft_model\n",
+        "from peft import PeftModel, PeftConfig\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "\n",
+        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
+        "config = PeftConfig.from_pretrained(peft_model_id)\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    config.base_model_name_or_path,\n",
+        "    return_dict=True,\n",
+        "    load_in_8bit=True,\n",
+        "    device_map=\"auto\",\n",
+        ")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+        "\n",
+        "# Tải Lora adapter\n",
+        "model = PeftModel.from_pretrained(model, peft_model_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MDqJWba-tpnv",
+        "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
+      },
+      "outputs": [],
+      "source": [
+        "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n",
+        "\n",
+        "with torch.cuda.amp.autocast():\n",
+        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
+        "\n",
+        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
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+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
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diff --git a/vi/3_parameter_efficient_finetuning/prompt_tuning.md b/vi/3_parameter_efficient_finetuning/prompt_tuning.md
new file mode 100644
index 00000000..241f624f
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/prompt_tuning.md
@@ -0,0 +1,76 @@
+# Phương Pháp Điều Chỉnh Chỉ Thị (Prompt Tuning)
+
+Prompt tuning là một phương pháp tiết kiệm tham số bằng cách điều chỉnh biểu diễn đầu vào thay vì trọng số mô hình. Không giống như tinh chỉnh truyền thống cập nhật tất cả các tham số mô hình, prompt tuning thêm và tối ưu hóa một tập nhỏ các token có thể huấn luyện trong khi giữ nguyên mô hình cơ sở.
+
+## Tìm Hiểu Về Prompt Tuning
+
+Prompt tuning là một phương pháp thay thế tiết kiệm tham số cho việc tinh chỉnh mô hình bằng cách thêm các vector liên tục có thể huấn luyện (soft prompts) vào trước văn bản đầu vào. Không giống như các chỉ thị rời rạc (discrete prompt), những soft prompt này được học thông qua truyền ngược (back-propagation) trong khi giữ nguyên mô hình ngôn ngữ. Phương pháp này được giới thiệu trong ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), cho thấy prompt tuning trở nên cạnh tranh hơn với việc tinh chỉnh mô hình khi kích thước mô hình tăng lên. Trong bài báo, với khoảng 10 tỷ tham số, prompt tuning đạt hiệu suất tương đương với tinh chỉnh mô hình trong khi chỉ sửa đổi vài trăm tham số cho mỗi tác vụ.
+
+Các soft prompt này là các vector liên tục trong không gian embedding của mô hình được tối ưu hóa trong quá trình huấn luyện. Không giống như các chỉ thị rời rạc truyền thống sử dụng các token ngôn ngữ tự nhiên, soft prompt là các chỉ thị không có ý nghĩa nhưng được học để tạo ra hành vi mong muốn từ mô hình đã đóng băng thông qua gradient descent. Kỹ thuật này đặc biệt hiệu quả cho các kịch bản đa tác vụ vì mỗi tác vụ chỉ cần lưu trữ một vector prompt nhỏ (thường là vài trăm tham số) thay vì một bản sao mô hình đầy đủ. Phương pháp này không chỉ duy trì bộ nhớ tối thiểu mà còn cho phép chuyển đổi tác vụ nhanh chóng bằng cách chỉ cần hoán đổi vector prompt mà không cần tải lại mô hình.
+
+## Quá trình huấn luyện
+
+Soft prompt thường có từ **8 đến 32 token** và có thể được **khởi tạo ngẫu nhiên hoặc từ văn bản hiện có**. Phương pháp khởi tạo đóng vai trò quan trọng trong quá trình huấn luyện, với việc khởi tạo dựa trên văn bản thường hoạt động tốt hơn so với khởi tạo ngẫu nhiên.
+
+Trong quá trình huấn luyện, chỉ các tham số prompt được cập nhật trong khi mô hình cơ sở vẫn đóng băng. Cách tiếp cận tập trung này sử dụng các mục tiêu huấn luyện tiêu chuẩn nhưng đòi hỏi chú ý đến tốc độ học và hành vi gradient của các token prompt.
+
+![prompt_tuning_explain](./images/prompt_tuning.png)
+
+## Triển khai với PEFT
+
+Thư viện PEFT giúp việc triển khai prompt tuning trở nên đơn giản. Đây là một ví dụ cơ bản:
+
+```python
+from peft import PromptTuningConfig, TaskType, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Tải mô hình cơ sở
+model = AutoModelForCausalLM.from_pretrained("your-base-model")
+tokenizer = AutoTokenizer.from_pretrained("your-base-model")
+
+# Điều chỉnh thông số prompt tuning
+peft_config = PromptTuningConfig(
+    task_type=TaskType.CAUSAL_LM,
+    num_virtual_tokens=8,  # Số token có thể huấn luyện
+    prompt_tuning_init="TEXT",  # Khởi tạo từ văn bản
+    prompt_tuning_init_text="Phân loại xem văn bản này là tích cực hay tiêu cực:",
+    tokenizer_name_or_path="your-base-model",
+)
+
+# Tạo mô hình có thể prompt-tune
+model = get_peft_model(model, peft_config)
+```
+
+## So sánh với các phương pháp khác
+
+Khi so sánh với các phương pháp PEFT khác, prompt tuning nổi bật về hiệu quả. Trong khi LoRA cung cấp số lượng tham số và bộ nhớ thấp nhưng yêu cầu tải adapter khi chuyển đổi tác vụ, prompt tuning đạt được mức sử dụng tài nguyên thậm chí thấp hơn và cho phép chuyển đổi tác vụ ngay lập tức. Ngược lại, tinh chỉnh đầy đủ đòi hỏi tài nguyên đáng kể và yêu cầu các bản sao mô hình riêng biệt cho các tác vụ khác nhau.
+
+| Phương pháp | Tham số | Bộ nhớ | Chuyển đổi tác vụ |
+|--------|------------|---------|----------------|
+| Prompt Tuning | Rất thấp | Tối thiểu | Dễ dàng |
+| LoRA | Thấp | Thấp | Yêu cầu tải |
+| Tinh chỉnh đầy đủ | Cao | Cao | Cần bản sao mô hình mới |
+
+Khi triển khai prompt tuning, hãy bắt đầu với một số lượng nhỏ token ảo (8-16) và chỉ tăng lên nếu độ phức tạp của tác vụ yêu cầu. Khởi tạo từ văn bản thường cho kết quả tốt hơn so với khởi tạo ngẫu nhiên, đặc biệt khi sử dụng văn bản liên quan đến tác vụ. Chiến lược khởi tạo nên phản ánh độ phức tạp của tác vụ mục tiêu của bạn.
+
+Huấn luyện đòi hỏi những cân nhắc hơi khác so với tinh chỉnh đầy đủ. Tốc độ học (learning rates) cao hơn thường sẽ cho kết quả tốt hơn, nhưng cũng cần theo dõi cẩn thận *gradient* của token prompt. Kiểm tra thường xuyên trên các ví dụ đa dạng giúp đảm bảo hiệu suất mạnh mẽ trong các tình huống khác nhau.
+
+## Ứng dụng
+
+Prompt tuning xuất sắc trong một số kịch bản:
+
+1. Triển khai đa tác vụ
+2. Môi trường hạn chế tài nguyên
+3. Thích ứng tác vụ nhanh chóng
+4. Ứng dụng nhạy cảm về quyền riêng tư
+
+Khi mô hình nhỏ hơn, prompt tuning trở nên kém cạnh tranh hơn so với tinh chỉnh đầy đủ. Ví dụ, trên các mô hình như SmolLM2, prompt tuning ít liên quan hơn so với tinh chỉnh đầy đủ.
+
+## Các bước tiếp theo
+
+⏭️ Chuyển sang [Hướng dẫn LoRA Adapters](./notebooks/finetune_sft_peft.ipynb) để tìm hiểu cách tinh chỉnh một mô hình với LoRA adapters.
+
+## Tài nguyên
+- [Tài liệu về thư viện PEFT](https://huggingface.co/docs/peft)
+- [Bài báo nghiên cứu về Prompt Tuning](https://arxiv.org/abs/2104.08691) 
+- [Hướng dẫn thực hành của Hugging Face](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
\ No newline at end of file
diff --git a/vi/README.md b/vi/README.md
index 3e3a5a17..ce109d5e 100644
--- a/vi/README.md
+++ b/vi/README.md
@@ -1,4 +1,4 @@
-![smolcourse image](./banner.png)
+![smolcourse image](../banner.png)
 
 # Khoá học Mô hình ngôn ngữ cơ bản
 
@@ -6,7 +6,7 @@
 
 *Lưu ý: Vì khóa học được dịch từ bản gốc tiếng Anh, chúng tôi sẽ giữ lại một số thuật ngữ gốc nhằm tránh gây hiểu lầm.*
 
-<a href="http://hf.co/join/discord">
+<a href="http://hf.co/join/discord">s
 <img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
 </a>
 

From 4cea4cc8e55c239d765b3857d1b1b340eb459a23 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Sun, 22 Dec 2024 00:06:06 +0100
Subject: [PATCH 09/63] typo

---
 vi/3_parameter_efficient_finetuning/README.md |     2 +-
 .../lora_adapters.md                          |     2 +-
 .../prompt_tuning.md                          |     2 +-
 vi/4_evaluation/README.md                     |    37 +
 vi/4_evaluation/automatic_benchmarks.md       |   131 +
 vi/4_evaluation/custom_evaluation.md          |   132 +
 ...hteval_evaluate_and_analyse_your_LLM.ipynb | 19422 ++++++++++++++++
 vi/4_evaluation/project/README.md             |    85 +
 vi/4_evaluation/project/annotate_dataset.py   |   129 +
 vi/4_evaluation/project/create_dataset.py     |    72 +
 vi/4_evaluation/project/evaluation_task.py    |    86 +
 vi/4_evaluation/project/generate_dataset.py   |   167 +
 .../images/domain_eval_argilla_view.png       |   Bin 0 -> 197693 bytes
 .../images/domain_eval_dataset_viewer.png     |   Bin 0 -> 89114 bytes
 14 files changed, 20264 insertions(+), 3 deletions(-)
 create mode 100644 vi/4_evaluation/README.md
 create mode 100644 vi/4_evaluation/automatic_benchmarks.md
 create mode 100644 vi/4_evaluation/custom_evaluation.md
 create mode 100644 vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
 create mode 100644 vi/4_evaluation/project/README.md
 create mode 100644 vi/4_evaluation/project/annotate_dataset.py
 create mode 100644 vi/4_evaluation/project/create_dataset.py
 create mode 100644 vi/4_evaluation/project/evaluation_task.py
 create mode 100644 vi/4_evaluation/project/generate_dataset.py
 create mode 100644 vi/4_evaluation/project/images/domain_eval_argilla_view.png
 create mode 100644 vi/4_evaluation/project/images/domain_eval_dataset_viewer.png

diff --git a/vi/3_parameter_efficient_finetuning/README.md b/vi/3_parameter_efficient_finetuning/README.md
index 207aa18d..447097e3 100644
--- a/vi/3_parameter_efficient_finetuning/README.md
+++ b/vi/3_parameter_efficient_finetuning/README.md
@@ -28,7 +28,7 @@ Prompt tuning cung cấp cách tiếp cận **thậm chí nhẹ hơn** bằng c
 | Tinh chỉnh LoRA | Học cách tinh chỉnh mô hình sử dụng LoRA adapters | 🐢 Huấn luyện mô hình sử dụng LoRA<br>🐕 Thử nghiệm với các giá trị rank khác nhau<br>🦁 So sánh hiệu suất với tinh chỉnh đầy đủ | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 | Tải LoRA Adapters | Học cách tải và sử dụng LoRA adapters đã huấn luyện | 🐢 Tải adapters đã huấn luyện trước<br>🐕 Gộp adapters với mô hình cơ sở<br>🦁 Chuyển đổi giữa nhiều adapters | [Notebook](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 
-## Tài nguyên
+## Tài liệu tham khảo
 - [Tài liệu PEFT](https://huggingface.co/docs/peft)
 - [Bài báo nghiên cứu LoRA](https://arxiv.org/abs/2106.09685)
 - [Bài báo nghiên cứu QLoRA](https://arxiv.org/abs/2305.14314)
diff --git a/vi/3_parameter_efficient_finetuning/lora_adapters.md b/vi/3_parameter_efficient_finetuning/lora_adapters.md
index 449dc2b9..d8fdafe4 100644
--- a/vi/3_parameter_efficient_finetuning/lora_adapters.md
+++ b/vi/3_parameter_efficient_finetuning/lora_adapters.md
@@ -117,7 +117,7 @@ tokenizer.save_pretrained("path/to/save/merged_model")
 ⏩ Chuyển sang [Hướng dẫn Prompt Tuning](prompt_tuning.md) để tìm hiểu cách tinh chỉnh một mô hình bằng prompt tuning.
 ⏩ Chuyển sang [Hướng dẫn lắp LoRA Adapters](./notebooks/load_lora_adapter.ipynb) để tìm hiểu cách lắp LoRA adapters.
 
-# Tài nguyên
+# Tài liệu tham khảo
 
 - [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
 - [Tài liệu PEFT](https://huggingface.co/docs/peft)
diff --git a/vi/3_parameter_efficient_finetuning/prompt_tuning.md b/vi/3_parameter_efficient_finetuning/prompt_tuning.md
index 241f624f..3e09a107 100644
--- a/vi/3_parameter_efficient_finetuning/prompt_tuning.md
+++ b/vi/3_parameter_efficient_finetuning/prompt_tuning.md
@@ -70,7 +70,7 @@ Khi mô hình nhỏ hơn, prompt tuning trở nên kém cạnh tranh hơn so v
 
 ⏭️ Chuyển sang [Hướng dẫn LoRA Adapters](./notebooks/finetune_sft_peft.ipynb) để tìm hiểu cách tinh chỉnh một mô hình với LoRA adapters.
 
-## Tài nguyên
+## Tài liệu tham khảo
 - [Tài liệu về thư viện PEFT](https://huggingface.co/docs/peft)
 - [Bài báo nghiên cứu về Prompt Tuning](https://arxiv.org/abs/2104.08691) 
 - [Hướng dẫn thực hành của Hugging Face](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
\ No newline at end of file
diff --git a/vi/4_evaluation/README.md b/vi/4_evaluation/README.md
new file mode 100644
index 00000000..525723c6
--- /dev/null
+++ b/vi/4_evaluation/README.md
@@ -0,0 +1,37 @@
+# Đánh giá mô hình
+
+Đánh giá là bước quan trọng trong việc phát triển và triển khai các mô hình ngôn ngữ. Nó giúp chúng ta hiểu được mô hình hoạt động tốt như thế nào qua các khả năng khác nhau và xác định những lĩnh vực cần cải thiện. Chương này bao gồm các phương pháp đánh giá (benchmark) tiêu chuẩn và các phương pháp đánh giá theo lĩnh vực cụ thể để đánh giá toàn diện mô hình của bạn.
+
+Chúng ta sẽ sử dụng [`lighteval`](https://github.com/huggingface/lighteval), một thư viện chuyên được sử dụng để đánh giá mô hình ngôn ngữ được phát triển bởi Hugging Face. Thư viện này được thiết kế tối giản giúp người sử dụng dễ dàng tiếp cận và được tích hợp để phù hợp với hệ sinh thái của Hugging Face. Để tìm hiểu sâu hơn về các khái niệm và thực hành về đánh giá mô hình ngôn ngữ, hãy tham khảo [hướng dẫn](https://github.com/huggingface/evaluation-guidebook) về đánh giá.
+
+## Tổng quan chương học
+
+Một quy trình đánh giá mô hình thường bao gồm nhiều khía cạnh về hiệu năng của mô hình. Chúng ta đánh giá các khả năng theo tác vụ cụ thể như *trả lời câu hỏi* và *tóm tắt* để hiểu mô hình xử lý các loại vấn đề khác nhau tốt đến đâu. Chúng ta đo lường chất lượng đầu ra thông qua các yếu tố như *tính mạch lạc* và *độ chính xác*. Đánh giá về *độ an toàn* giúp xác định các đầu ra của mô hình có chứa các hành vi khuyển khích thực hiện hành đông độc hại hoặc định kiến tiềm ẩn. Cuối cùng, kiểm tra *chuyên môn* về lĩnh vực xác minh kiến thức chuyên biệt của mô hình trong lĩnh vực mục tiêu của bạn.
+
+## Nội dung
+
+### 1️⃣ [Đánh giá mô hình tự động](./automatic_benchmarks.md)
+
+Học cách đánh giá mô hình của bạn bằng các phương pháp đánh giá và số liệu chuẩn hoá. Chúng ta sẽ khám phá các phương pháp đánh giá phổ biến như `MMLU` và `TruthfulQA`, hiểu các chỉ số đánh giá và cài đặt quan trọng, đồng thời đề cập đến các thực hành tốt nhất cho việc đánh giá có thể tái tạo lại.
+
+### 2️⃣ [Đánh giá theo yêu cầu đặc biệt](./custom_evaluation.md)
+Khám phá cách tạo quy trình (pipeline) đánh giá phù hợp với trường hợp sử dụng cụ thể của bạn. Chúng ta sẽ hướng dẫn thiết kế các tác vụ đánh giá theo yêu cầu (custom), triển khai các số liệu chuyên biệt và xây dựng tập dữ liệu đánh giá phù hợp với yêu cầu của bạn.
+
+### 3️⃣ [Dự án đánh giá theo lĩnh vực cụ thể](./project/README.md)
+Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh giá cho lĩnh vực cụ thể. Bạn sẽ học cách tạo tập dữ liệu đánh giá, sử dụng `Argilla` để gán nhãn dữ liệu, tạo tập dữ liệu chuẩn hoá và đánh giá mô hình bằng `LightEval`.
+
+### Các notebook bài tập
+
+| Tiêu đề | Mô tả | Bài tập | Link | Colab |
+|---------|-------|---------|------|-------|
+| Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng LightEval để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong lĩnh vực y tế để đánh giá mô hình <br> 🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau <br> 🦁 Tạo tác vụ đánh giá tùy chỉnh cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## Tài nguyên
+
+- [Hướng dẫn đánh giá](https://github.com/huggingface/evaluation-guidebook) - Hướng dẫn toàn diện về đánh giá LLM
+- [Tài liệu LightEval](https://github.com/huggingface/lighteval) - Tài liệu chính thức cho thư viện LightEval 
+- [Tài liệu Argilla](https://docs.argilla.io) - Tìm hiểu về nền tảng gán nhãn Argilla
+- [Paper MMLU](https://arxiv.org/abs/2009.03300) - Paper mô tả benchmark MMLU
+- [Tạo tác vụ tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Tạo số liệu tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Sử dụng số liệu có sẵn](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file
diff --git a/vi/4_evaluation/automatic_benchmarks.md b/vi/4_evaluation/automatic_benchmarks.md
new file mode 100644
index 00000000..6a980fa4
--- /dev/null
+++ b/vi/4_evaluation/automatic_benchmarks.md
@@ -0,0 +1,131 @@
+# Automatic Benchmarks
+
+Automatic benchmarks serve as standardized tools for evaluating language models across different tasks and capabilities. While they provide a useful starting point for understanding model performance, it's important to recognize that they represent only one piece of a comprehensive evaluation strategy.
+
+## Understanding Automatic Benchmarks
+
+Automatic benchmarks typically consist of curated datasets with predefined tasks and evaluation metrics. These benchmarks aim to assess various aspects of model capability, from basic language understanding to complex reasoning. The key advantage of using automatic benchmarks is their standardization - they allow for consistent comparison across different models and provide reproducible results.
+
+However, it's crucial to understand that benchmark performance doesn't always translate directly to real-world effectiveness. A model that excels at academic benchmarks may still struggle with specific domain applications or practical use cases.
+
+## Benchmarks and Their Limitations
+
+### General Knowledge Benchmarks
+
+MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects, from science to humanities. While comprehensive, it may not reflect the depth of expertise needed for specific domains. TruthfulQA evaluates a model's tendency to reproduce common misconceptions, though it can't capture all forms of misinformation.
+
+### Reasoning Benchmarks
+BBH (Big Bench Hard) and GSM8K focus on complex reasoning tasks. BBH tests logical thinking and planning, while GSM8K specifically targets mathematical problem-solving. These benchmarks help assess analytical capabilities but may not capture the nuanced reasoning required in real-world scenarios.
+
+### Language Understanding
+HELM provides a holistic evaluation framework, while WinoGrande tests common sense through pronoun disambiguation. These benchmarks offer insights into language processing capabilities but may not fully represent the complexity of natural conversation or domain-specific terminology.
+
+## Alternative Evaluation Approaches
+
+Many organizations have developed alternative evaluation methods to address the limitations of standard benchmarks:
+
+### LLM-as-Judge
+Using one language model to evaluate another's outputs has become increasingly popular. This approach can provide more nuanced feedback than traditional metrics, though it comes with its own biases and limitations.
+
+### Evaluation Arenas
+Platforms like Anthropic's Constitutional AI Arena allow models to interact and evaluate each other in controlled environments. This can reveal strengths and weaknesses that might not be apparent in traditional benchmarks.
+
+### Custom Benchmark Suites
+Organizations often develop internal benchmark suites tailored to their specific needs and use cases. These might include domain-specific knowledge tests or evaluation scenarios that mirror actual deployment conditions.
+
+## Creating Your Own Evaluation Strategy
+
+Remember that while LightEval makes it easy to run standard benchmarks, you should also invest time in developing evaluation methods specific to your use case.
+
+While standard benchmarks provide a useful baseline, they shouldn't be your only evaluation method. Here's how to develop a more comprehensive approach:
+
+1. Start with relevant standard benchmarks to establish a baseline and enable comparison with other models.
+
+2. Identify the specific requirements and challenges of your use case. What tasks will your model actually perform? What kinds of errors would be most problematic?
+
+3. Develop custom evaluation datasets that reflect your actual use case. This might include:
+   - Real user queries from your domain
+   - Common edge cases you've encountered
+   - Examples of particularly challenging scenarios
+
+4. Consider implementing a multi-layered evaluation strategy:
+   - Automated metrics for quick feedback
+   - Human evaluation for nuanced understanding
+   - Domain expert review for specialized applications
+   - A/B testing in controlled environments
+
+## Using LightEval for Benchmarking
+
+LightEval tasks are defined using a specific format:
+```
+{suite}|{task}|{num_few_shot}|{auto_reduce}
+```
+
+- **suite**: The benchmark suite (e.g., 'mmlu', 'truthfulqa')
+- **task**: Specific task within the suite (e.g., 'abstract_algebra')
+- **num_few_shot**: Number of examples to include in prompt (0 for zero-shot)
+- **auto_reduce**: Whether to automatically reduce few-shot examples if prompt is too long (0 or 1)
+
+Example: `"mmlu|abstract_algebra|0|0"` evaluates on MMLU's abstract algebra task with zero-shot inference.
+
+### Example Evaluation Pipeline
+
+Here's a complete example of setting up and running an evaluation on automatic benchmarks relevant to one specific domain:
+
+```python
+from lighteval.tasks import Task, Pipeline
+from transformers import AutoModelForCausalLM
+
+# Define tasks to evaluate
+domain_tasks = [
+    "mmlu|anatomy|0|0",
+    "mmlu|high_school_biology|0|0", 
+    "mmlu|high_school_chemistry|0|0",
+    "mmlu|professional_medicine|0|0"
+]
+
+# Configure pipeline parameters
+pipeline_params = {
+    "max_samples": 40,  # Number of samples to evaluate
+    "batch_size": 1,    # Batch size for inference
+    "num_workers": 4    # Number of worker processes
+}
+
+# Create evaluation tracker
+evaluation_tracker = EvaluationTracker(
+    output_path="./results",
+    save_generations=True
+)
+
+# Load model and create pipeline
+model = AutoModelForCausalLM.from_pretrained("your-model-name")
+pipeline = Pipeline(
+    tasks=domain_tasks,
+    pipeline_parameters=pipeline_params,
+    evaluation_tracker=evaluation_tracker,
+    model=model
+)
+
+# Run evaluation
+pipeline.evaluate()
+
+# Get and display results
+results = pipeline.get_results()
+pipeline.show_results()
+```
+
+Results are displayed in a tabular format showing:
+```
+|                  Task                  |Version|Metric|Value |   |Stderr|
+|----------------------------------------|------:|------|-----:|---|-----:|
+|all                                     |       |acc   |0.3333|±  |0.1169|
+|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
+|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
+|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
+```
+
+You can also handle the results in a pandas DataFrame and visualise or represent them as you want.
+
+# Next Steps
+
+⏩ Explore [Custom Domain Evaluation](./custom_evaluation.md) to learn how to create evaluation pipelines tailored to your specific needs
diff --git a/vi/4_evaluation/custom_evaluation.md b/vi/4_evaluation/custom_evaluation.md
new file mode 100644
index 00000000..c82868a7
--- /dev/null
+++ b/vi/4_evaluation/custom_evaluation.md
@@ -0,0 +1,132 @@
+# Custom Domain Evaluation
+
+While standard benchmarks provide valuable insights, many applications require specialized evaluation approaches tailored to specific domains or use cases. This guide will help you create custom evaluation pipelines that accurately assess your model's performance in your target domain.
+
+## Designing Your Evaluation Strategy
+
+A successful custom evaluation strategy starts with clear objectives. Consider what specific capabilities your model needs to demonstrate in your domain. This might include technical knowledge, reasoning patterns, or domain-specific formats. Document these requirements carefully - they'll guide both your task design and metric selection.
+
+Your evaluation should test both standard use cases and edge cases. For example, in a medical domain, you might evaluate both common diagnostic scenarios and rare conditions. In financial applications, you might test both routine transactions and complex edge cases involving multiple currencies or special conditions.
+
+## Implementation with LightEval
+
+LightEval provides a flexible framework for implementing custom evaluations. Here's how to create a custom task:
+
+```python
+from lighteval.tasks import Task, Doc
+from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
+
+class CustomEvalTask(Task):
+    def __init__(self):
+        super().__init__(
+            name="custom_task",
+            version="0.0.1",
+            metrics=["accuracy", "f1"],  # Your chosen metrics
+            description="Description of your custom evaluation task"
+        )
+    
+    def get_prompt(self, sample):
+        # Format your input into a prompt
+        return f"Question: {sample['question']}\nAnswer:"
+    
+    def process_response(self, response, ref):
+        # Process model output and compare to reference
+        return response.strip() == ref.strip()
+```
+
+## Custom Metrics
+
+Domain-specific tasks often require specialized metrics. LightEval provides a flexible framework for creating custom metrics that capture domain-relevant aspects of performance:
+
+```python
+from aenum import extend_enum
+from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
+import numpy as np
+
+# Define a sample-level metric function
+def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
+    """Example metric that returns multiple scores per sample"""
+    response = predictions[0]
+    return {
+        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
+        "length_match": len(response) == len(formatted_doc.reference)
+    }
+
+# Create a metric that returns multiple values per sample
+custom_metric_group = SampleLevelMetricGrouping(
+    metric_name=["accuracy", "length_match"],  # Names of sub-metrics
+    higher_is_better={  # Whether higher values are better for each metric
+        "accuracy": True,
+        "length_match": True
+    },
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=custom_metric,
+    corpus_level_fn={  # How to aggregate each metric
+        "accuracy": np.mean,
+        "length_match": np.mean
+    }
+)
+
+# Register the metric with LightEval
+extend_enum(Metrics, "custom_metric_name", custom_metric_group)
+```
+
+For simpler cases where you only need one metric value per sample:
+
+```python
+def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
+    """Example metric that returns a single score per sample"""
+    response = predictions[0]
+    return response == formatted_doc.choices[formatted_doc.gold_index]
+
+simple_metric_obj = SampleLevelMetric(
+    metric_name="simple_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=simple_metric,
+    corpus_level_fn=np.mean  # How to aggregate across samples
+)
+
+extend_enum(Metrics, "simple_metric", simple_metric_obj)
+```
+
+You can then use your custom metrics in your evaluation tasks by referencing them in the task configuration. The metrics will be automatically computed across all samples and aggregated according to your specified functions.
+
+For more complex metrics, consider:
+- Using metadata in your formatted documents to weight or adjust scores
+- Implementing custom aggregation functions for corpus-level statistics
+- Adding validation checks for your metric inputs
+- Documenting edge cases and expected behavior
+
+For a complete example of custom metrics in action, see our [domain evaluation project](./project/README.md).
+
+## Dataset Creation
+
+High-quality evaluation requires carefully curated datasets. Consider these approaches for dataset creation:
+
+1. Expert Annotation: Work with domain experts to create and validate evaluation examples. Tools like [Argilla](https://github.com/argilla-io/argilla) make this process more efficient.
+
+2. Real-World Data: Collect and anonymize real usage data, ensuring it represents actual deployment scenarios.
+
+3. Synthetic Generation: Use LLMs to generate initial examples, then have experts validate and refine them.
+
+## Best Practices
+
+- Document your evaluation methodology thoroughly, including any assumptions or limitations
+- Include diverse test cases that cover different aspects of your domain
+- Consider both automated metrics and human evaluation where appropriate
+- Version control your evaluation datasets and code
+- Regularly update your evaluation suite as you discover new edge cases or requirements
+
+## References
+
+- [LightEval Custom Task Guide](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [LightEval Custom Metrics](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Argilla Documentation](https://docs.argilla.io) for dataset annotation
+- [Evaluation Guidebook](https://github.com/huggingface/evaluation-guidebook) for general evaluation principles
+
+# Next Steps
+
+⏩ For a complete example of implementing these concepts, see our [domain evaluation project](./project/README.md).
\ No newline at end of file
diff --git a/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
new file mode 100644
index 00000000..9597634b
--- /dev/null
+++ b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
@@ -0,0 +1,19422 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TZVw9f5QYWPL"
+      },
+      "source": [
+        "# lighteval is your AI evaluation library\n",
+        "\n",
+        "This notebook explores how you can use lighteval to evaluate and compare LLMs.\n",
+        "\n",
+        "`lighteval` has been around a while and it's a great tool for getting eval score on major benchmarks. It's just been refactored to support being used like a library in Python, which makes it great for comparing models across benchmarks.\n",
+        "\n",
+        "So let's dig in to some eval scores.\n",
+        "\n",
+        "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+        "    <h2 style='margin: 0;color:blue'>Exercise: Evaluate Your Own Model</h2>\n",
+        "    <p>Now that you've seen how to evaluate models on specific domains, try evaluating a model on a domain that interests you.</p>\n",
+        "    <p><b>Difficulty Levels</b></p>\n",
+        "    <p>🐢 Use the existing medical domain tasks but evaluate a different model from the Hugging Face hub</p>\n",
+        "    <p>🐕 Create a new domain evaluation by selecting different MMLU tasks (e.g., computer science, mathematics, physics)</p>\n",
+        "    <p>🦁 Create a custom evaluation task using LightEval's task framework and evaluate models on your specific domain</p>\n",
+        "</div>\n",
+        "\n",
+        "## Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "afW_CLJoPCnF",
+        "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n",
+        "!pip list | grep torch"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8xMnn_cQ1EEi",
+        "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TDKs5ShvXw8K"
+      },
+      "source": [
+        "## Setup `lighteval` evaluation\n",
+        "\n",
+        "We need to setup the evaluation environment and pipeline. Much of this we will disable because we're keeping things in the notebook, but we could also use `push_to_hub` or `push_to_tensorboard`.\n",
+        "\n",
+        "### `push_to_hub`\n",
+        "\n",
+        "This is useful if we're evaluating a model and want to persist its evaluation with weights and configuration on the Hugging Face hub.\n",
+        "\n",
+        "### `push_to_tensorboard`\n",
+        "\n",
+        "This would be useful if we were building an evaluation tool or script, where we wanted to view results within tensorboard."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296,
+          "referenced_widgets": [
+            "711ff69b059c421f84405dc798c84297",
+            "3c90ad18d29042caa880291056159985",
+            "66d9640217374065a9af74765929d326",
+            "5965b90591764931a635cc9305f2fa40",
+            "b34849662b994d87a912fdee127e1eed",
+            "1c358b2f428a4bb9bc0245b94d6394e8",
+            "9e9b42ee82614d56ab89f79bc2639d29",
+            "39db106760854445bc1d1c0a246400f9",
+            "737225a54b724c60ac0fb543b94bd39a",
+            "b6b2341d52504399840f08eb6d25bd84",
+            "4d137ae7c6e442f380e7764a741572d6",
+            "f837792155cc4e93884c80bd0bad7881",
+            "0ed8df4245654e928f9c9b584e02e203",
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+          ]
+        },
+        "id": "3cUebd-z6IWs",
+        "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e"
+      },
+      "outputs": [],
+      "source": [
+        "import lighteval\n",
+        "import os\n",
+        "from datetime import timedelta\n",
+        "from transformers import AutoModelForCausalLM\n",
+        "\n",
+        "from lighteval.logging.evaluation_tracker import EvaluationTracker\n",
+        "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n",
+        "from lighteval.models.model_config import create_model_config\n",
+        "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n",
+        "\n",
+        "TOKEN = os.getenv(\"HF_TOKEN\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "muikmXNQXgFv"
+      },
+      "outputs": [],
+      "source": [
+        "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n",
+        "\n",
+        "evaluation_tracker = EvaluationTracker(\n",
+        "    output_dir=\"~/tmp\",\n",
+        "    save_details=False,\n",
+        "    push_to_hub=False,\n",
+        "    push_to_tensorboard=False,\n",
+        "    public=False,\n",
+        "    hub_results_org=False,\n",
+        ")\n",
+        "\n",
+        "pipeline_params = PipelineParameters(\n",
+        "    launcher_type=ParallelismManager.ACCELERATE,\n",
+        "    env_config=env_config,\n",
+        "    job_id=1,\n",
+        "    override_batch_size=1,\n",
+        "    num_fewshot_seeds=0,\n",
+        "    max_samples=10,\n",
+        "    use_chat_template=False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nsNjwzCtltkA"
+      },
+      "source": [
+        "# Compares models with `lighteval`\n",
+        "\n",
+        "We are going to compare two small LLMs on a domain. We will use `Qwen2.5-0.5B` and `SmolLM2-360M-Instruct` and we will evaluate them on a medical domain.\n",
+        "\n",
+        "We can create a domain evaluation from a subset of MMLU evaluations, by defining the evaluation tasks. In lighteval, tasks are described as strings.\n",
+        "\n",
+        "`{suite}|{task}:{subtask}|{num_few_shot}|{0 or 1 to reduce num_few_shot if prompt is too long}`\n",
+        "\n",
+        "Therefore, we will pass our list of medicine related tasks like this:\n",
+        "\n",
+        "```\n",
+        "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
+        "```\n",
+        "\n",
+        "Which can be translated to :\n",
+        "\n",
+        "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n",
+        "|---|---|---|---|\n",
+        "| leaderboard | mmlu:anatomy | 5 | False |\n",
+        "| leaderboard | mmlu:professional_medicine | 5 | False |\n",
+        "| leaderboard | mmlu:high_school_biology | 5 | False |\n",
+        "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n",
+        "\n",
+        "For a full list of lighteval supported tasks. Checkout this page in [the documentation](https://github.com/huggingface/lighteval/wiki/Available-Tasks)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qTqsizv9mVbO"
+      },
+      "outputs": [],
+      "source": [
+        "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XwcJklSFX4H6"
+      },
+      "source": [
+        "# Evaluate Qwen2.5 0.5B"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
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+          "height": 1000,
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+          ]
+        },
+        "id": "PXJuaXVxUNBO",
+        "outputId": "87aca361-5bd5-4b12-9097-3e97f1165d7b"
+      },
+      "outputs": [],
+      "source": [
+        "qwen_model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen2.5-0.5B\")\n",
+        "\n",
+        "pipeline = Pipeline(\n",
+        "    tasks=domain_tasks,\n",
+        "    pipeline_parameters=pipeline_params,\n",
+        "    evaluation_tracker=evaluation_tracker,\n",
+        "    model=qwen_model\n",
+        ")\n",
+        "\n",
+        "pipeline.evaluate()\n",
+        "\n",
+        "qwen_results = pipeline.get_results()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CIwCaCxJX_hA"
+      },
+      "source": [
+        "# Evaluate SmolLM 360M"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+        "id": "Dxg0RtlNVT4y",
+        "outputId": "ee40393c-bf62-499d-d443-e96da72411e8"
+      },
+      "outputs": [],
+      "source": [
+        "smol_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n",
+        "\n",
+        "pipeline = Pipeline(\n",
+        "    tasks=domain_tasks,\n",
+        "    pipeline_parameters=pipeline_params,\n",
+        "    evaluation_tracker=evaluation_tracker,\n",
+        "    model=smol_model\n",
+        ")\n",
+        "\n",
+        "pipeline.evaluate()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pdjyzfKHVt52"
+      },
+      "outputs": [],
+      "source": [
+        "smol_results = pipeline.get_results()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eugvMFfgV1VD",
+        "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96"
+      },
+      "outputs": [],
+      "source": [
+        "pipeline.show_results()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0HD8aFwSYGHu"
+      },
+      "source": [
+        "# Visualize Results\n",
+        "\n",
+        "Now that we have results from the two models we can visualize them side-by-side. We'll keep visualisation simple here, but with this data structure you could represent scores in many ways."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "1sReqrgQUO9r",
+        "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n",
+        "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n",
+        "df = pd.concat([df, _df], axis=1)\n",
+        "df.plot(kind=\"barh\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qJEbQeYDplKX"
+      },
+      "source": [
+        "# 💐 That's it!\n",
+        "\n",
+        "You have a handy notebook to view model evals. You could use this to:\n",
+        "\n",
+        "- select the right model for your inference use case\n",
+        "- evaluate checkpoints during training\n",
+        "- share model scores"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jWdS38syaipm"
+      },
+      "source": [
+        "🏃Next Steps\n",
+        "\n",
+        "- If you want to go deeper into your evaluation results check out this [notebook](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n"
+      ]
+    }
+  ],
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diff --git a/vi/4_evaluation/project/README.md b/vi/4_evaluation/project/README.md
new file mode 100644
index 00000000..98d32eca
--- /dev/null
+++ b/vi/4_evaluation/project/README.md
@@ -0,0 +1,85 @@
+# Domain Specific Evaluation with Argilla, Distilabel, and LightEval
+
+Most popular benchmarks look at very general capabilities (reasoning, math, code), but have you ever needed to study more specific capabilities? 
+
+What should you do if you need to evaluate a model on a **custom domain** relevant to your use-cases? (For example, financial, legal, medical use cases)  
+
+This tutorial shows you the full pipeline you can follow, from creating relevant data and annotating your samples to evaluating your model on them, with the easy to use [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), and [lighteval](https://github.com/huggingface/lighteval). For our example, we'll focus on generating exam questions from multiple documents. 
+
+## Project Structure
+
+For our process, we will follow 4 steps, with a script for each: generating a dataset, annotating it, extracting relevant samples for evaluation from it, and actually evaluating models.
+
+| Script Name | Description |
+|-------------|-------------|
+| generate_dataset.py | Generates exam questions from multiple text documents using a specified language model. |
+| annotate_dataset.py | Creates an Argilla dataset for manual annotation of the generated exam questions. |
+| create_dataset.py | Processes annotated data from Argilla and creates a Hugging Face dataset. |
+| evaluation_task.py | Defines a custom LightEval task for evaluating language models on the exam questions dataset. |
+
+## Steps
+
+### 1. Generate Dataset
+
+The `generate_dataset.py` script uses the distilabel library to generate exam questions based on multiple text documents. It uses the specified model (default: Meta-Llama-3.1-8B-Instruct) to create questions, correct answers, and incrorect answers (known as distractors). You should add you own data samples and you might wish to use a different model.
+
+To run the generation:
+
+```sh
+python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
+```
+
+This will create a [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) containing the generated exam questions for all documents in the input directory. 
+
+### 2. Annotate Dataset
+
+The `annotate_dataset.py` script takes the generated questions and creates an Argilla dataset for annotation. It sets up the dataset structure and populates it with the generated questions and answers, randomizing the order of answers to avoid bias. Once in Argilla, you or a domain expert can validate the dataset with the correct answers.
+
+You will see suggested correct answers from the LLM in random order and you can approve the correct answer or select a different one. The duration of this process will depend on the scale of your evaluation dataset, the complexity of your domain data, and the quality of your LLM. For example, we were able to create 150 samples within 1 hour on the domain of transfer learning, using Llama-3.1-70B-Instruct, mostly by approving the correct answer and discarding the incorrect ones.
+
+To run the annotation process:
+
+```sh
+python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
+```
+
+This will create an Argilla dataset that can be used for manual review and annotation.
+
+![argilla_dataset](./images/domain_eval_argilla_view.png)
+
+If you're not using Argilla, deploy it locally or on spaces following this [quickstart guide](https://docs.argilla.io/latest/getting_started/quickstart/).
+
+### 3. Create Dataset
+
+The `create_dataset.py` script processes the annotated data from Argilla and creates a Hugging Face dataset. It handles both suggested and manually annotated answers. The script will create a dataset with the question, possible answers, and the column name for the correct answer. To create the final dataset:
+
+```sh
+huggingface_hub login
+python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
+```
+
+This will push the dataset to the Hugging Face Hub under the specified repository. You can view the sample dataset on the hub [here](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), and a preview of the dataset looks like this:
+
+![hf_dataset](./images/domain_eval_dataset_viewer.png)
+
+### 4. Evaluation Task
+
+The `evaluation_task.py` script defines a custom LightEval task for evaluating language models on the exam questions dataset. It includes a prompt function, a custom accuracy metric, and the task configuration. 
+
+To evaluate a model using lighteval with the custom exam questions task:
+
+```sh
+lighteval accelerate \
+    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
+    --tasks "community|exam_questions|0|0" \
+    --custom_tasks domain-eval/evaluation_task.py \
+    --output_dir "./evals"
+```
+
+You can find detailed guides in lighteval wiki about each of these steps: 
+
+- [Creating a Custom Task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Creating a Custom Metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Using existing metrics](https://github.com/huggingface/lighteval/wiki/Metric-List)
+
+
diff --git a/vi/4_evaluation/project/annotate_dataset.py b/vi/4_evaluation/project/annotate_dataset.py
new file mode 100644
index 00000000..dadffc10
--- /dev/null
+++ b/vi/4_evaluation/project/annotate_dataset.py
@@ -0,0 +1,129 @@
+import argparse
+import json
+from random import choices, sample
+
+import argilla as rg
+from distilabel.distiset import Distiset
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Annotate exam questions dataset using Argilla."
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="API key for Argilla",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="API URL for Argilla",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Path to the exam questions dataset",
+)
+parser.add_argument(
+    "--dataset_config",
+    type=str,
+    default="default",
+    help="Configuration of the dataset",
+)
+parser.add_argument(
+    "--dataset_split",
+    type=str,
+    default="train",
+    help="Split of the dataset to use",
+)
+parser.add_argument(
+    "--output_dataset_name",
+    type=str,
+    default="exam_questions",
+    help="Name of the output Argilla dataset",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Create Argilla dataset with the feedback task for validation
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+
+if client.datasets(args.output_dataset_name):
+    print(f"Deleting existing dataset '{args.output_dataset_name}'")
+    client.datasets(args.output_dataset_name).delete()
+
+settings = rg.Settings(
+    fields=[
+        rg.TextField("question"),
+        rg.TextField("answer_a"),
+        rg.TextField("answer_b"),
+        rg.TextField("answer_c"),
+        rg.TextField("answer_d"),
+    ],
+    questions=[
+        rg.LabelQuestion(
+            name="correct_answer",
+            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
+        ),
+        rg.TextQuestion(
+            name="improved_question",
+            description="Could you improve the question?",
+        ),
+        rg.TextQuestion(
+            name="improved_answer",
+            description="Could you improve the best answer?",
+        ),
+    ],
+)
+
+dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
+dataset.create()
+
+################################################################################
+# Load the Distiset and process and add records to Argilla dataset
+# We will validate that questions appear in random order to avoid bias
+# but we will show correct answers in the Argilla UI as suggestions.
+################################################################################
+
+distiset = Distiset.load_from_disk(args.dataset_path)
+answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
+dataset_records = []
+
+for exam in distiset[args.dataset_config][args.dataset_split]:
+    exam_json = json.loads(exam["generation"])["exam"]
+
+    for question in exam_json:
+        answer = question["answer"]
+        distractors = question["distractors"]
+        distractors = choices(distractors, k=3)
+        answers = distractors + [answer]
+        answers = sample(answers, len(answers))
+        suggestion_idx = answers.index(answer)
+        fields = dict(zip(answer_names, answers))
+        fields["question"] = question["question"]
+
+        record = rg.Record(
+            fields=fields,
+            suggestions=[
+                rg.Suggestion(
+                    question_name="correct_answer",
+                    value=answer_names[suggestion_idx],
+                )
+            ],
+        )
+        dataset_records.append(record)
+
+dataset.records.log(dataset_records)
+
+print(
+    f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla."
+)
diff --git a/vi/4_evaluation/project/create_dataset.py b/vi/4_evaluation/project/create_dataset.py
new file mode 100644
index 00000000..822e268e
--- /dev/null
+++ b/vi/4_evaluation/project/create_dataset.py
@@ -0,0 +1,72 @@
+import argparse
+
+import argilla as rg
+from datasets import Dataset
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Create a Hugging Face dataset from annotated Argilla data."
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="API key for Argilla",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="API URL for Argilla",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Path to the Argilla dataset",
+)
+parser.add_argument(
+    "--dataset_repo_id",
+    type=str,
+    default="burtenshaw/exam_questions",
+    help="Hugging Face dataset repository ID",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Initialize Argilla client and load dataset
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+dataset = client.datasets(args.dataset_path)
+
+################################################################################
+# Process Argilla records
+################################################################################
+
+dataset_rows = []
+
+for record in dataset.records(with_suggestions=True, with_responses=True):
+    row = record.fields
+
+    if len(record.responses) == 0:
+        answer = record.suggestions["correct_answer"].value
+        row["correct_answer"] = answer
+    else:
+        for response in record.responses:
+            if response.question_name == "correct_answer":
+                row["correct_answer"] = response.value
+    dataset_rows.append(row)
+
+################################################################################
+# Create Hugging Face dataset and push to Hub
+################################################################################
+
+hf_dataset = Dataset.from_list(dataset_rows)
+hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
+
+print(f"Dataset has been successfully pushed to {args.dataset_repo_id}")
diff --git a/vi/4_evaluation/project/evaluation_task.py b/vi/4_evaluation/project/evaluation_task.py
new file mode 100644
index 00000000..397c75ad
--- /dev/null
+++ b/vi/4_evaluation/project/evaluation_task.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+from lighteval.metrics.utils.metric_utils import (
+    SampleLevelMetric,
+    MetricCategory,
+    MetricUseCase,
+)
+
+################################################################################
+# Define the prompt function based on the structure of the dataset
+################################################################################
+
+
+def prompt_fn(line, task_name: str = None):
+    """Converts a dataset line to a Doc object for evaluation."""
+    instruction = "Choose the correct answer for the following exam question:"
+    return Doc(
+        task_name=task_name,
+        query=f"{instruction} {line['question']}",
+        choices=[
+            f" {line['answer_a']}",
+            f" {line['answer_b']}",
+            f" {line['answer_c']}",
+            f" {line['answer_d']}",
+        ],
+        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
+            line["correct_answer"]
+        ),
+        instruction=instruction,
+    )
+
+
+################################################################################
+# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
+# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List
+# Existing metrics can be imported from lighteval.metrics.metrics
+################################################################################
+
+
+def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
+    response = np.argmin(kwargs["choices_logprob"])
+    return response == formatted_doc.gold_index
+
+
+custom_metric = SampleLevelMetric(
+    metric_name="exam_question_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.MULTICHOICE,
+    use_case=MetricUseCase.NONE,
+    sample_level_fn=sample_level_fn,
+    corpus_level_fn=np.mean,
+)
+
+################################################################################
+# Define the task based on the prompt function and the custom metric
+# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
+################################################################################
+
+task = LightevalTaskConfig(
+    name="example",
+    prompt_function=prompt_fn,
+    suite=["community"],
+    hf_repo="burtenshaw/exam_questions",
+    hf_subset="default",
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    few_shots_split=None,
+    few_shots_select=None,
+    metric=[custom_metric],
+)
+
+# Add the task to TASKS_TABLE
+TASKS_TABLE = [task]
+
+# MODULE LOGIC
+if __name__ == "__main__":
+    print([t.name for t in TASKS_TABLE])
+    print(len(TASKS_TABLE))
+
+# lighteval accelerate \
+# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
+# "community|example|0|0" \
+# --custom-tasks "submitted_tasks/example.py" \
+# --output-dir "results"
diff --git a/vi/4_evaluation/project/generate_dataset.py b/vi/4_evaluation/project/generate_dataset.py
new file mode 100644
index 00000000..cb531d3f
--- /dev/null
+++ b/vi/4_evaluation/project/generate_dataset.py
@@ -0,0 +1,167 @@
+import argparse
+import os
+from pydantic import BaseModel, Field
+from datasets import Dataset
+
+from distilabel.llms import InferenceEndpointsLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps.tasks import TextGeneration
+
+
+################################################################################
+# Script Parameters
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Generate exam questions from text files in a directory."
+)
+parser.add_argument(
+    "--model_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="Model ID for text generation",
+)
+parser.add_argument(
+    "--tokenizer_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="Tokenizer ID for text generation",
+)
+parser.add_argument(
+    "--input_dir",
+    type=str,
+    help="Directory containing input text files",
+    default="data",
+)
+parser.add_argument(
+    "--max_new_tokens",
+    type=int,
+    default=2048,
+    help="Maximum number of new tokens to generate",
+)
+parser.add_argument(
+    "--output_path",
+    type=str,
+    default="exam_questions_output",
+    help="Directory to save the generated datasets",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Load the documents
+# We assume that the documents are in the input directory, and that each file
+# is a separate document about the same topic.
+################################################################################
+
+# Process all text files in the input directory
+documents = []
+for filename in os.listdir(args.input_dir):
+    if filename.endswith(".txt"):
+        file_path = os.path.join(args.input_dir, filename)
+        with open(file=file_path, mode="r", encoding="utf-8") as file:
+            document_content = file.read()
+            documents.append(document_content)
+
+# Create a single dataset from all document contents
+dataset = Dataset.from_dict({"document": documents})
+
+################################################################################
+# Define the prompts
+# We use a system prompt to guide the model to generate the correct output format.
+# A template is used to insert the document into the prompt.
+################################################################################
+
+SYSTEM_PROMPT = """\
+You are an exam writer specialized in writing exams for students.
+Your goal is to create questions and answers based on the document provided, 
+and a list of distractors, that are incorrect but viable answers to the question.
+Your answer must adhere to the following format:
+```
+[
+    {
+        "question": "Your question",
+        "answer": "The correct answer to the question",
+        "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"]
+    },
+    ... (more questions and answers as required)
+]
+```
+""".strip()
+
+INSTRUCTION_TEMPLATE = """\
+    Generate a list of answers and questions about the document. 
+    Document:\n\n{{ instruction }}"""
+
+################################################################################
+# Define the output structure
+# We define a data model for the output of the pipeline, this is used to ensure
+# that the output is in the correct format for the evaluation task.
+################################################################################
+
+
+class ExamQuestion(BaseModel):
+    question: str = Field(..., description="The question to be answered")
+    answer: str = Field(..., description="The correct answer to the question")
+    distractors: List[str] = Field(
+        ..., description="A list of incorrect but viable answers to the question"
+    )
+
+
+class ExamQuestions(BaseModel):
+    exam: List[ExamQuestion]
+
+
+################################################################################
+# Create the pipeline
+# We create a pipeline with a single task that generates the exam questions
+# based on the document and in the correct format. We will Hugging Face
+# InferenceEndpoints and the model specified in the arguments.
+################################################################################
+
+with Pipeline(
+    name="Domain-Eval-Questions",
+    description="Generate exam questions based on given documents.",
+) as pipeline:
+    # Set up the text generation task
+    text_generation = TextGeneration(
+        name="exam_generation",
+        llm=InferenceEndpointsLLM(
+            model_id=args.model_id,
+            tokenizer_id=args.model_id,
+            api_key=os.environ["HF_TOKEN"],
+            structured_output={
+                "schema": ExamQuestions.model_json_schema(),
+                "format": "json",
+            },
+        ),
+        input_batch_size=8,
+        output_mappings={"model_name": "generation_model"},
+        input_mappings={"instruction": "document"},
+        system_prompt=SYSTEM_PROMPT,
+        template=INSTRUCTION_TEMPLATE,
+    )
+
+
+################################################################################
+# Run the pipeline
+# We run the pipeline for all documents and save the results to the output path.
+################################################################################
+
+if __name__ == "__main__":
+    # Run the pipeline for all documents
+    distiset = pipeline.run(
+        parameters={
+            "exam_generation": {
+                "llm": {
+                    "generation_kwargs": {
+                        "max_new_tokens": args.max_new_tokens,
+                    }
+                }
+            }
+        },
+        use_cache=False,
+        dataset=dataset,
+    )
+
+    distiset.save_to_disk(args.output_path)
diff --git a/vi/4_evaluation/project/images/domain_eval_argilla_view.png b/vi/4_evaluation/project/images/domain_eval_argilla_view.png
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From 5d7fa1388b17fae5abd14b585900bababe78db86 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Sun, 22 Dec 2024 00:07:33 +0100
Subject: [PATCH 10/63] typo

---
 vi/4_evaluation/README.md | 2 +-
 vi/README.md              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/vi/4_evaluation/README.md b/vi/4_evaluation/README.md
index 525723c6..04aedd6a 100644
--- a/vi/4_evaluation/README.md
+++ b/vi/4_evaluation/README.md
@@ -26,7 +26,7 @@ Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh
 |---------|-------|---------|------|-------|
 | Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng LightEval để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong lĩnh vực y tế để đánh giá mô hình <br> 🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau <br> 🦁 Tạo tác vụ đánh giá tùy chỉnh cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 
-## Tài nguyên
+## Tài liệu tham khảo
 
 - [Hướng dẫn đánh giá](https://github.com/huggingface/evaluation-guidebook) - Hướng dẫn toàn diện về đánh giá LLM
 - [Tài liệu LightEval](https://github.com/huggingface/lighteval) - Tài liệu chính thức cho thư viện LightEval 
diff --git a/vi/README.md b/vi/README.md
index ce109d5e..77913375 100644
--- a/vi/README.md
+++ b/vi/README.md
@@ -88,7 +88,7 @@ Nếu dùng **Google Colab** bạn sẽ cần cài đặt dependencies một cá
 pip install -r transformers trl datasets huggingface_hub
 ```
 
-## Tham gia
+## Tham gia và chia sẻ
 
 Hãy chia sẻ khoá học này, để cùng nhau phát triển cộng đồng AI Việt Nam.
 

From a933c4dc95c27fdbb958e5b3767e62bb73078114 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Sun, 22 Dec 2024 00:09:26 +0100
Subject: [PATCH 11/63] init

---
 vi/4_evaluation/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vi/4_evaluation/README.md b/vi/4_evaluation/README.md
index 04aedd6a..27b796da 100644
--- a/vi/4_evaluation/README.md
+++ b/vi/4_evaluation/README.md
@@ -24,7 +24,7 @@ Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh
 
 | Tiêu đề | Mô tả | Bài tập | Link | Colab |
 |---------|-------|---------|------|-------|
-| Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng LightEval để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong lĩnh vực y tế để đánh giá mô hình <br> 🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau <br> 🦁 Tạo tác vụ đánh giá tùy chỉnh cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng `LightEval` để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong **lĩnh vực y tế** để đánh giá mô hình <br> 🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau <br> 🦁 Tạo tác vụ đánh giá cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 
 ## Tài liệu tham khảo
 
@@ -32,6 +32,6 @@ Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh
 - [Tài liệu LightEval](https://github.com/huggingface/lighteval) - Tài liệu chính thức cho thư viện LightEval 
 - [Tài liệu Argilla](https://docs.argilla.io) - Tìm hiểu về nền tảng gán nhãn Argilla
 - [Paper MMLU](https://arxiv.org/abs/2009.03300) - Paper mô tả benchmark MMLU
-- [Tạo tác vụ tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Tạo số liệu tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Tạo tác vụ theo yêu cầu](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Tạo tiêu chuẩn đánh giá theo yêu cầu](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
 - [Sử dụng số liệu có sẵn](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file

From 0ea6dcf15ee63ef62e9a9fd40411ee9f9cd307d1 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Sun, 22 Dec 2024 01:21:00 +0100
Subject: [PATCH 12/63] init

---
 vi/4_evaluation/automatic_benchmarks.md | 112 ++++++++++++------------
 1 file changed, 56 insertions(+), 56 deletions(-)

diff --git a/vi/4_evaluation/automatic_benchmarks.md b/vi/4_evaluation/automatic_benchmarks.md
index 6a980fa4..b5852e9c 100644
--- a/vi/4_evaluation/automatic_benchmarks.md
+++ b/vi/4_evaluation/automatic_benchmarks.md
@@ -1,82 +1,82 @@
-# Automatic Benchmarks
+# Đánh giá tự động
 
-Automatic benchmarks serve as standardized tools for evaluating language models across different tasks and capabilities. While they provide a useful starting point for understanding model performance, it's important to recognize that they represent only one piece of a comprehensive evaluation strategy.
+Đánh giá tự động là các công cụ chuẩn hoá để đánh giá các mô hình ngôn ngữ qua các tác vụ và khả năng khác nhau. Mặc dù chúng cung cấp điểm khởi đầu hữu ích để hiểu hiệu năng của mô hình, điều quan trọng là phải nhận ra rằng chúng chỉ là một phần trong toàn bộ khả năng của mô hình.
 
-## Understanding Automatic Benchmarks
+## Hiểu về đánh giá tự động
 
-Automatic benchmarks typically consist of curated datasets with predefined tasks and evaluation metrics. These benchmarks aim to assess various aspects of model capability, from basic language understanding to complex reasoning. The key advantage of using automatic benchmarks is their standardization - they allow for consistent comparison across different models and provide reproducible results.
+Đánh giá tự động thường bao gồm các tập dữ liệu được tuyển chọn với các tác vụ và tiêu chí đánh giá được định nghĩa trước. Những đánh giá này nhằm đánh giá nhiều khía cạnh về khả năng của mô hình, từ hiểu ngôn ngữ cơ bản đến suy luận phức tạp. Lợi thế chính của việc sử dụng đánh giá tự động là tính chuẩn hoá - chúng cho phép so sánh nhất quán giữa các mô hình khác nhau và cung cấp kết quả có thể tái tạo lại.
 
-However, it's crucial to understand that benchmark performance doesn't always translate directly to real-world effectiveness. A model that excels at academic benchmarks may still struggle with specific domain applications or practical use cases.
+Tuy nhiên, điều quan trọng là phải hiểu rằng điểm số trên các bài đánh giá không phải lúc nào cũng thể hiện chính xác hiệu quả của mô hình trong thực tế. Một mô hình xuất sắc trong các bài đánh giá học thuật (academic benchmark) vẫn có thể gặp khó khăn với các ứng dụng vào lĩnh vực cụ thể hoặc các trường hợp sử dụng thực tế.
 
-## Benchmarks and Their Limitations
+## Các bài đánh giá và hạn chế của chúng
 
-### General Knowledge Benchmarks
+### Đánh giá kiến thức tổng quát
 
-MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects, from science to humanities. While comprehensive, it may not reflect the depth of expertise needed for specific domains. TruthfulQA evaluates a model's tendency to reproduce common misconceptions, though it can't capture all forms of misinformation.
+`MMLU` (Massive Multitask Language Understanding) là bài đánh giá dùng để kiểm tra kiến thức trên *57 môn học*, từ khoa học tự nhiên đến khoa học xã hội. Mặc dù toàn diện, nó có thể không phản ánh độ sâu của chuyên môn cần thiết cho các lĩnh vực cụ thể. `TruthfulQA` đánh giá xu hướng mô hình tái tạo các quan niệm sai lầm phổ biến, mặc dù nó không thể nắm bắt tất cả các hình thức thông tin sai lệch.
 
-### Reasoning Benchmarks
-BBH (Big Bench Hard) and GSM8K focus on complex reasoning tasks. BBH tests logical thinking and planning, while GSM8K specifically targets mathematical problem-solving. These benchmarks help assess analytical capabilities but may not capture the nuanced reasoning required in real-world scenarios.
+### Đánh giá khả năng suy luận
+`BBH` (Big Bench Hard) và `GSM8K` tập trung vào các tác vụ suy luận phức tạp. `BBH` kiểm tra tư duy logic và lập kế hoạch, trong khi `GSM8K` nhắm vào giải quyết vấn đề toán học. Những bài đánh giá này giúp kiểm tra khả năng phân tích nhưng có thể không nắm bắt được khả năng suy luận tinh tế cần thiết trong các tình huống thực tế.
 
-### Language Understanding
-HELM provides a holistic evaluation framework, while WinoGrande tests common sense through pronoun disambiguation. These benchmarks offer insights into language processing capabilities but may not fully represent the complexity of natural conversation or domain-specific terminology.
+### Đánh giá khả năng ngôn ngữ
+`HELM` cung cấp một đánh giá toàn diện, trong khi `WinoGrande` kiểm tra hiểu biết thông thường thông qua việc giải nghĩa các đại từ. Những bài đánh giá này cung cấp cái nhìn sâu sắc về khả năng xử lý ngôn ngữ nhưng có thể không hoàn toàn đại diện cho độ phức tạp của giao tiếp tự nhiên hoặc thuật ngữ theo lĩnh vực cụ thể.
 
-## Alternative Evaluation Approaches
+## Các phương pháp đánh giá thay thế
 
-Many organizations have developed alternative evaluation methods to address the limitations of standard benchmarks:
+Nhiều tổ chức đã phát triển các phương pháp đánh giá thay thế để khắc phục những hạn chế của bài đánh giá tiêu chuẩn:
 
-### LLM-as-Judge
-Using one language model to evaluate another's outputs has become increasingly popular. This approach can provide more nuanced feedback than traditional metrics, though it comes with its own biases and limitations.
+### LLM trong vài trò người đánh giá 
+Sử dụng một mô hình ngôn ngữ để đánh giá đầu ra của mô hình khác ngày càng phổ biến. Phương pháp này có thể cung cấp phản hồi chi tiết hơn các bài đánh giá truyền thống, mặc dù nó đi kèm với những định kiến (bias) của mô hình đánh giá và hạn chế riêng.
 
-### Evaluation Arenas
-Platforms like Anthropic's Constitutional AI Arena allow models to interact and evaluate each other in controlled environments. This can reveal strengths and weaknesses that might not be apparent in traditional benchmarks.
+### Môi trường đánh giá
+Các nền tảng như Constitutional AI Arena của Anthropic cho phép các mô hình tương tác và đánh giá lẫn nhau trong môi trường được kiểm soát. Điều này có thể cho thấy những điểm mạnh và điểm yếu có thể không rõ ràng trong các benchmark truyền thống.
 
-### Custom Benchmark Suites
-Organizations often develop internal benchmark suites tailored to their specific needs and use cases. These might include domain-specific knowledge tests or evaluation scenarios that mirror actual deployment conditions.
+### Bộ đánh giá tuỳ chỉnh
+Các tổ chức thường phát triển bộ benchmark nội bộ được điều chỉnh cho nhu cầu và trường hợp sử dụng cụ thể của họ. Những bộ này có thể bao gồm kiểm tra kiến thức theo lĩnh vực cụ thể hoặc các kịch bản đánh giá phản ánh điều kiện triển khai thực tế.
 
-## Creating Your Own Evaluation Strategy
+## Tạo chiến lược đánh giá riêng
 
-Remember that while LightEval makes it easy to run standard benchmarks, you should also invest time in developing evaluation methods specific to your use case.
+Hãy nhớ rằng mặc dù LightEval giúp dễ dàng chạy các benchmark tiêu chuẩn, bạn cũng nên dành thời gian phát triển phương pháp đánh giá phù hợp với trường hợp sử dụng của mình.
 
-While standard benchmarks provide a useful baseline, they shouldn't be your only evaluation method. Here's how to develop a more comprehensive approach:
+Mặc dù benchmark tiêu chuẩn cung cấp một đường cơ sở hữu ích, chúng không nên là phương pháp đánh giá duy nhất của bạn. Dưới đây là cách phát triển một phương pháp toàn diện hơn:
 
-1. Start with relevant standard benchmarks to establish a baseline and enable comparison with other models.
+1. Bắt đầu với các benchmark tiêu chuẩn liên quan để thiết lập đường cơ sở và cho phép so sánh với các mô hình khác.
 
-2. Identify the specific requirements and challenges of your use case. What tasks will your model actually perform? What kinds of errors would be most problematic?
+2. Xác định các yêu cầu và thách thức cụ thể của trường hợp sử dụng của bạn. Mô hình của bạn sẽ thực hiện những tác vụ gì? Những loại lỗi nào sẽ gây rắc rối nhất?
 
-3. Develop custom evaluation datasets that reflect your actual use case. This might include:
-   - Real user queries from your domain
-   - Common edge cases you've encountered
-   - Examples of particularly challenging scenarios
+3. Phát triển tập dữ liệu đánh giá tuỳ chỉnh phản ánh trường hợp sử dụng thực tế của bạn. Điều này có thể bao gồm:
+   - Các câu hỏi thực tế từ người dùng trong lĩnh vực của bạn
+   - Các trường hợp ngoại lệ phổ biến bạn đã gặp
+   - Các ví dụ về tình huống đặc biệt khó khăn
 
-4. Consider implementing a multi-layered evaluation strategy:
-   - Automated metrics for quick feedback
-   - Human evaluation for nuanced understanding
-   - Domain expert review for specialized applications
-   - A/B testing in controlled environments
+4. Xem xét triển khai chiến lược đánh giá nhiều lớp:
+   - Số liệu tự động để nhận phản hồi nhanh
+   - Đánh giá bởi con người để hiểu sâu sắc hơn
+   - Đánh giá của chuyên gia lĩnh vực cho các ứng dụng chuyên biệt
+   - Kiểm tra A/B trong môi trường được kiểm soát
 
-## Using LightEval for Benchmarking
+## Sử dụng LightEval để đánh giá
 
-LightEval tasks are defined using a specific format:
+Các tác vụ LightEval được định nghĩa theo định dạng cụ thể:
 ```
 {suite}|{task}|{num_few_shot}|{auto_reduce}
 ```
 
-- **suite**: The benchmark suite (e.g., 'mmlu', 'truthfulqa')
-- **task**: Specific task within the suite (e.g., 'abstract_algebra')
-- **num_few_shot**: Number of examples to include in prompt (0 for zero-shot)
-- **auto_reduce**: Whether to automatically reduce few-shot examples if prompt is too long (0 or 1)
+- **suite**: Bộ benchmark (ví dụ: 'mmlu', 'truthfulqa')  
+- **task**: Tác vụ cụ thể trong bộ (ví dụ: 'abstract_algebra')
+- **num_few_shot**: Số lượng ví dụ để đưa vào prompt (0 cho zero-shot)
+- **auto_reduce**: Có tự động giảm ví dụ few-shot nếu prompt quá dài hay không (0 hoặc 1)
 
-Example: `"mmlu|abstract_algebra|0|0"` evaluates on MMLU's abstract algebra task with zero-shot inference.
+Ví dụ: `"mmlu|abstract_algebra|0|0"` đánh giá tác vụ đại số trừu tượng của MMLU với suy luận zero-shot.
 
-### Example Evaluation Pipeline
+### Ví dụ về Pipeline đánh giá 
 
-Here's a complete example of setting up and running an evaluation on automatic benchmarks relevant to one specific domain:
+Đây là một ví dụ hoàn chỉnh về việc thiết lập và chạy đánh giá trên các benchmark tự động liên quan đến một lĩnh vực cụ thể:
 
 ```python
 from lighteval.tasks import Task, Pipeline
 from transformers import AutoModelForCausalLM
 
-# Define tasks to evaluate
+# Định nghĩa các tác vụ để đánh giá
 domain_tasks = [
     "mmlu|anatomy|0|0",
     "mmlu|high_school_biology|0|0", 
@@ -84,20 +84,20 @@ domain_tasks = [
     "mmlu|professional_medicine|0|0"
 ]
 
-# Configure pipeline parameters
+# Cấu hình tham số pipeline
 pipeline_params = {
-    "max_samples": 40,  # Number of samples to evaluate
-    "batch_size": 1,    # Batch size for inference
-    "num_workers": 4    # Number of worker processes
+    "max_samples": 40,  # Số lượng mẫu để đánh giá
+    "batch_size": 1,    # Kích thước batch cho inference 
+    "num_workers": 4    # Số lượng worker process
 }
 
-# Create evaluation tracker
+# Tạo evaluation tracker
 evaluation_tracker = EvaluationTracker(
     output_path="./results",
     save_generations=True
 )
 
-# Load model and create pipeline
+# Tải mô hình và tạo pipeline
 model = AutoModelForCausalLM.from_pretrained("your-model-name")
 pipeline = Pipeline(
     tasks=domain_tasks,
@@ -106,15 +106,15 @@ pipeline = Pipeline(
     model=model
 )
 
-# Run evaluation
+# Chạy đánh giá
 pipeline.evaluate()
 
-# Get and display results
+# Lấy và hiển thị kết quả 
 results = pipeline.get_results()
 pipeline.show_results()
 ```
 
-Results are displayed in a tabular format showing:
+Kết quả được hiển thị dưới dạng bảng thể hiện:
 ```
 |                  Task                  |Version|Metric|Value |   |Stderr|
 |----------------------------------------|------:|------|-----:|---|-----:|
@@ -124,8 +124,8 @@ Results are displayed in a tabular format showing:
 |leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
 ```
 
-You can also handle the results in a pandas DataFrame and visualise or represent them as you want.
+Bạn cũng có thể xử lý kết quả trong pandas DataFrame và trực quan hoá hoặc biểu diễn chúng theo cách bạn muốn.
 
-# Next Steps
+# Bước tiếp theo
 
-⏩ Explore [Custom Domain Evaluation](./custom_evaluation.md) to learn how to create evaluation pipelines tailored to your specific needs
+⏩ Khám phá [Đánh giá theo lĩnh vực tuỳ chỉnh](./custom_evaluation.md) để học cách tạo pipeline đánh giá phù hợp với nhu cầu cụ thể của bạn
\ No newline at end of file

From d428ec44ed80515207b5ae63e6906f227b87cd39 Mon Sep 17 00:00:00 2001
From: duydl <duy.dole.00ece@gmail.com>
Date: Sun, 22 Dec 2024 15:08:16 +0700
Subject: [PATCH 13/63] Add video usage

---
 .../notebooks/vlm_usage_sample.ipynb          | 267 +++++++++++++++---
 1 file changed, 227 insertions(+), 40 deletions(-)

diff --git a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
index 437d5f68..dab4ca8e 100644
--- a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -6,18 +6,20 @@
    "source": [
     "# Processing images and text with VLMs \n",
     "\n",
-    "This notebook demonstrates how to utilize the `HuggingFaceTB/SmolVLM-Instruct` 4bit-quantized model for various multimodal tasks."
+    "This notebook demonstrates how to utilize the `HuggingFaceTB/SmolVLM-Instruct` 4bit-quantized model for various multimodal tasks.\n",
+    "\n",
+    "generating image captions, answering questions based on visuals, or understanding the relationship between textual and visual data. object detection"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "42a6f26e0f59460ea9eaacb7bec95f31",
+       "model_id": "e441ea20de5241bfbcd385e9dfb5cd17",
        "version_major": 2,
        "version_minor": 0
       },
@@ -34,15 +36,13 @@
     "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
     "\n",
     "# Authenticate to Hugging Face\n",
-    "from huggingface_hub import login\n",
-    "login()\n",
-    "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+    "from huggingface_hub import notebook_login\n",
+    "notebook_login()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -53,11 +53,17 @@
       "You shouldn't move a model that is dispatched using accelerate hooks.\n",
       "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
      ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'longest_edge': 1536}\n"
+     ]
     }
    ],
    "source": [
-    "import torch\n",
-    "from PIL import Image\n",
+    "import torch, PIL\n",
     "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
     "from transformers.image_utils import load_image\n",
     "\n",
@@ -73,7 +79,9 @@
     "    model_name,\n",
     "    quantization_config=quantization_config,\n",
     ").to(device)\n",
-    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")"
+    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
+    "\n",
+    "print(processor.image_processor.size)"
    ]
   },
   {
@@ -85,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -111,18 +119,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -137,9 +133,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -180,14 +184,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['User:<image>Can you describe the two images?\\nAssistant: The first image shows a child wearing a red hat and holding a red hat in front of a fireplace. The second image shows a present with a pine cone and a Christmas decoration.']\n"
+      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
      ]
     }
    ],
@@ -198,22 +202,22 @@
     "\n",
     "# Create input messages\n",
     "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
-    "        ]\n",
-    "    },\n",
     "    # {\n",
     "    #     \"role\": \"user\",\n",
     "    #     \"content\": [\n",
     "    #         {\"type\": \"image\"},\n",
     "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
+    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
     "    #     ]\n",
     "    # },\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
+    "        ]\n",
+    "    },\n",
     "]\n",
     "\n",
     "# Prepare inputs\n",
@@ -231,19 +235,202 @@
     "print(generated_texts)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>Describe the image?\\nAssistant: The image is a Christmas-themed greeting card. The background is black, and the text \"Merry Christmas and a Happy New Year\" is written in gold letters. The text is surrounded by various Christmas-themed decorations, including a candy cane, a star, a Christmas tree, a reindeer, a present, a Christmas ornament, a holly leaf, and a Christmas ornament. The decorations are all gold and have a shiny, reflective appearance. The image is very festive and has a warm, happy, and inviting tone.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
+    "display(Image(url=document_image_url))\n",
+    "\n",
+    "# Load the document image\n",
+    "document_image = load_image(document_image_url)\n",
+    "\n",
+    "# Create input message for analysis\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"Describe the image?\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# Prepare inputs\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# Generate outputs\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Processing videos"
+    "## Processing videos\n",
+    "\n",
+    "\n",
+    "<video width=\"640\" height=\"360\" controls>\n",
+    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
+    "  Your browser does not support the video tag.\n",
+    "</video>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from IPython.display import Video\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "\n",
+    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
+    "    cap = cv2.VideoCapture(video_path)\n",
+    "    if not cap.isOpened():\n",
+    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
+    "    \n",
+    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
+    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
+    "\n",
+    "    frames = []\n",
+    "    for idx in frame_indices:\n",
+    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
+    "        ret, frame = cap.read()\n",
+    "        if ret:\n",
+    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
+    "            if target_size:\n",
+    "                frames.append(resize_and_crop(frame, target_size))\n",
+    "            else:\n",
+    "                frames.append(frame)\n",
+    "    cap.release()\n",
+    "    return frames\n",
+    "\n",
+    "def resize_and_crop(image, target_size):\n",
+    "    width, height = image.size\n",
+    "    scale = target_size / min(width, height)\n",
+    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
+    "    left = (image.width - target_size) // 2\n",
+    "    top = (image.height - target_size) // 2\n",
+    "    return image.crop((left, top, left + target_size, top + target_size))\n",
+    "\n",
+    "# Video link\n",
+    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OutOfMemoryError",
+     "evalue": "CUDA out of memory. Tried to allocate 3.96 GiB. GPU 0 has a total capacity of 11.75 GiB of which 3.77 GiB is free. Including non-PyTorch memory, this process has 6.59 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1.31 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 30\u001b[0m\n\u001b[1;32m     28\u001b[0m processor\u001b[38;5;241m.\u001b[39mimage_processor\u001b[38;5;241m.\u001b[39mdo_resize \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Generate response\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_response\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocessor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# Display the result\u001b[39;00m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;66;03m# print(\"Question:\", question)\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResponse:\u001b[39m\u001b[38;5;124m\"\u001b[39m, response)\n",
+      "Cell \u001b[0;32mIn[11], line 18\u001b[0m, in \u001b[0;36mgenerate_response\u001b[0;34m(model, processor, frames, question)\u001b[0m\n\u001b[1;32m      6\u001b[0m messages \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      7\u001b[0m     {\n\u001b[1;32m      8\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      9\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: [{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFollowing are the frames of a video in temporal order.\u001b[39m\u001b[38;5;124m\"\u001b[39m}, \u001b[38;5;241m*\u001b[39mimage_tokens, {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m: question}]\n\u001b[1;32m     10\u001b[0m     }\n\u001b[1;32m     11\u001b[0m ]\n\u001b[1;32m     12\u001b[0m inputs \u001b[38;5;241m=\u001b[39m processor(\n\u001b[1;32m     13\u001b[0m     text\u001b[38;5;241m=\u001b[39mprocessor\u001b[38;5;241m.\u001b[39mapply_chat_template(messages, add_generation_prompt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m     14\u001b[0m     images\u001b[38;5;241m=\u001b[39mframes,\n\u001b[1;32m     15\u001b[0m     return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     16\u001b[0m )\u001b[38;5;241m.\u001b[39mto(model\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m---> 18\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_beams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.7\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdo_sample\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     20\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m processor\u001b[38;5;241m.\u001b[39mdecode(outputs[\u001b[38;5;241m0\u001b[39m], skip_special_tokens\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/generation/utils.py:2246\u001b[0m, in \u001b[0;36mGenerationMixin.generate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   2238\u001b[0m     input_ids, model_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expand_inputs_for_generation(\n\u001b[1;32m   2239\u001b[0m         input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m   2240\u001b[0m         expand_size\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mnum_beams,\n\u001b[1;32m   2241\u001b[0m         is_encoder_decoder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m   2242\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[1;32m   2243\u001b[0m     )\n\u001b[1;32m   2245\u001b[0m     \u001b[38;5;66;03m# 13. run beam sample\u001b[39;00m\n\u001b[0;32m-> 2246\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_beam_search\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2247\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeam_scorer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlogits_processor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_logits_processor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstopping_criteria\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_stopping_criteria\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgeneration_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgeneration_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2252\u001b[0m \u001b[43m        \u001b[49m\u001b[43msynced_gpus\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msynced_gpus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2254\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2256\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m generation_mode \u001b[38;5;241m==\u001b[39m GenerationMode\u001b[38;5;241m.\u001b[39mGROUP_BEAM_SEARCH:\n\u001b[1;32m   2257\u001b[0m     \u001b[38;5;66;03m# 11. prepare beam search scorer\u001b[39;00m\n\u001b[1;32m   2258\u001b[0m     beam_scorer \u001b[38;5;241m=\u001b[39m BeamSearchScorer(\n\u001b[1;32m   2259\u001b[0m         batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m   2260\u001b[0m         num_beams\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mnum_beams,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2266\u001b[0m         max_length\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mmax_length,\n\u001b[1;32m   2267\u001b[0m     )\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/generation/utils.py:3455\u001b[0m, in \u001b[0;36mGenerationMixin._beam_search\u001b[0;34m(self, input_ids, beam_scorer, logits_processor, stopping_criteria, generation_config, synced_gpus, **model_kwargs)\u001b[0m\n\u001b[1;32m   3452\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m stack_model_outputs(outputs_per_sub_batch, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget_text_config())\n\u001b[1;32m   3454\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# Unchanged original behavior\u001b[39;00m\n\u001b[0;32m-> 3455\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   3457\u001b[0m \u001b[38;5;66;03m# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping\u001b[39;00m\n\u001b[1;32m   3458\u001b[0m model_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_model_kwargs_for_generation(\n\u001b[1;32m   3459\u001b[0m     outputs,\n\u001b[1;32m   3460\u001b[0m     model_kwargs,\n\u001b[1;32m   3461\u001b[0m     is_encoder_decoder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m   3462\u001b[0m )\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:1196\u001b[0m, in \u001b[0;36mIdefics3ForConditionalGeneration.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, pixel_values, pixel_attention_mask, image_hidden_states, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1193\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[1;32m   1195\u001b[0m \u001b[38;5;66;03m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[39;00m\n\u001b[0;32m-> 1196\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1197\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1198\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1199\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1200\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1201\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1203\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpixel_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1204\u001b[0m \u001b[43m    \u001b[49m\u001b[43mimage_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1205\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1206\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1207\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1208\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1209\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1211\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1212\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlm_head(hidden_states)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:1000\u001b[0m, in \u001b[0;36mIdefics3Model.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, pixel_values, pixel_attention_mask, image_hidden_states, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    997\u001b[0m patch_attention_mask \u001b[38;5;241m=\u001b[39m (patches_subgrid\u001b[38;5;241m.\u001b[39msum(dim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mbool()\n\u001b[1;32m    999\u001b[0m \u001b[38;5;66;03m# Get sequence from the vision encoder\u001b[39;00m\n\u001b[0;32m-> 1000\u001b[0m image_hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1001\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1002\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpatch_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpatch_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1003\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mlast_hidden_state\n\u001b[1;32m   1005\u001b[0m \u001b[38;5;66;03m# Modality projection & resampling\u001b[39;00m\n\u001b[1;32m   1006\u001b[0m image_hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconnector(image_hidden_states)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:728\u001b[0m, in \u001b[0;36mIdefics3VisionTransformer.forward\u001b[0;34m(self, pixel_values, patch_attention_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    725\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_use_flash_attention_2:\n\u001b[1;32m    726\u001b[0m     patch_attention_mask \u001b[38;5;241m=\u001b[39m _prepare_4d_attention_mask(patch_attention_mask, hidden_states\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[0;32m--> 728\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    729\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    730\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpatch_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    731\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    732\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    733\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    734\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    736\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    737\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_layernorm(last_hidden_state)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:518\u001b[0m, in \u001b[0;36mIdefics3Encoder.forward\u001b[0;34m(self, inputs_embeds, attention_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    511\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    512\u001b[0m         encoder_layer\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    513\u001b[0m         hidden_states,\n\u001b[1;32m    514\u001b[0m         attention_mask,\n\u001b[1;32m    515\u001b[0m         output_attentions,\n\u001b[1;32m    516\u001b[0m     )\n\u001b[1;32m    517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 518\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mencoder_layer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    519\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    520\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    522\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    524\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    526\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:430\u001b[0m, in \u001b[0;36mIdefics3EncoderLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m    427\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[1;32m    429\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer_norm1(hidden_states)\n\u001b[0;32m--> 430\u001b[0m hidden_states, attn_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself_attn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    435\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n\u001b[1;32m    437\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:250\u001b[0m, in \u001b[0;36mIdefics3VisionAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m    247\u001b[0m     attn_weights \u001b[38;5;241m=\u001b[39m attn_weights \u001b[38;5;241m+\u001b[39m attention_mask\n\u001b[1;32m    249\u001b[0m \u001b[38;5;66;03m# upcast attention to fp32\u001b[39;00m\n\u001b[0;32m--> 250\u001b[0m attn_weights \u001b[38;5;241m=\u001b[39m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctional\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msoftmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattn_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(query_states\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m    251\u001b[0m attn_weights \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mfunctional\u001b[38;5;241m.\u001b[39mdropout(attn_weights, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining)\n\u001b[1;32m    252\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmatmul(attn_weights, value_states)\n",
+      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/functional.py:2142\u001b[0m, in \u001b[0;36msoftmax\u001b[0;34m(input, dim, _stacklevel, dtype)\u001b[0m\n\u001b[1;32m   2140\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39msoftmax(dim)\n\u001b[1;32m   2141\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2142\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msoftmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
+      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 3.96 GiB. GPU 0 has a total capacity of 11.75 GiB of which 3.77 GiB is free. Including non-PyTorch memory, this process has 6.59 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1.31 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"Describe what the woman is doing.\"\n",
+    "\n",
+    "def generate_response(model, processor, frames, question):\n",
+    "\n",
+    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
+    "    messages = [\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
+    "        }\n",
+    "    ]\n",
+    "    inputs = processor(\n",
+    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
+    "        images=frames,\n",
+    "        return_tensors=\"pt\"\n",
+    "    ).to(model.device)\n",
+    "\n",
+    "    outputs = model.generate(\n",
+    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
+    "    )\n",
+    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
+    "\n",
+    "\n",
+    "# Extract frames from the video\n",
+    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
+    "\n",
+    "processor.image_processor.size = (384, 384)\n",
+    "processor.image_processor.do_resize = False\n",
+    "# Generate response\n",
+    "response = generate_response(model, processor, frames, question)\n",
+    "\n",
+    "# Display the result\n",
+    "# print(\"Question:\", question)\n",
+    "print(\"Response:\", response)"
+   ]
   }
  ],
  "metadata": {
@@ -262,7 +449,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,

From b09436d0469ae8737a83bd296a6229c51caf2b0d Mon Sep 17 00:00:00 2001
From: duydl <duy.dole.00ece@gmail.com>
Date: Sun, 22 Dec 2024 15:18:43 +0700
Subject: [PATCH 14/63] Improve desc

---
 .../notebooks/vlm_usage_sample.ipynb          | 78 +++++++++----------
 1 file changed, 36 insertions(+), 42 deletions(-)

diff --git a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
index dab4ca8e..8cd2b85d 100644
--- a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -19,7 +19,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e441ea20de5241bfbcd385e9dfb5cd17",
+       "model_id": "cd19d9e0d17b4e349f1d60a4fb5c79a4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -91,6 +91,13 @@
     "## Processing Images"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let try question answering on some image. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -182,6 +189,13 @@
     "print(generated_texts)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could also input multiple image. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -235,9 +249,16 @@
     "print(generated_texts)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "VLM also have great OCR capability. Try experiment with image with denser text."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -256,7 +277,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['User:<image>Describe the image?\\nAssistant: The image is a Christmas-themed greeting card. The background is black, and the text \"Merry Christmas and a Happy New Year\" is written in gold letters. The text is surrounded by various Christmas-themed decorations, including a candy cane, a star, a Christmas tree, a reindeer, a present, a Christmas ornament, a holly leaf, and a Christmas ornament. The decorations are all gold and have a shiny, reflective appearance. The image is very festive and has a warm, happy, and inviting tone.']\n"
+      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
      ]
     }
    ],
@@ -273,7 +294,7 @@
     "        \"role\": \"user\",\n",
     "        \"content\": [\n",
     "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Describe the image?\"}\n",
+    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
     "        ]\n",
     "    }\n",
     "]\n",
@@ -351,47 +372,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "ename": "OutOfMemoryError",
-     "evalue": "CUDA out of memory. Tried to allocate 3.96 GiB. GPU 0 has a total capacity of 11.75 GiB of which 3.77 GiB is free. Including non-PyTorch memory, this process has 6.59 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1.31 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[11], line 30\u001b[0m\n\u001b[1;32m     28\u001b[0m processor\u001b[38;5;241m.\u001b[39mimage_processor\u001b[38;5;241m.\u001b[39mdo_resize \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Generate response\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_response\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocessor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# Display the result\u001b[39;00m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;66;03m# print(\"Question:\", question)\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResponse:\u001b[39m\u001b[38;5;124m\"\u001b[39m, response)\n",
-      "Cell \u001b[0;32mIn[11], line 18\u001b[0m, in \u001b[0;36mgenerate_response\u001b[0;34m(model, processor, frames, question)\u001b[0m\n\u001b[1;32m      6\u001b[0m messages \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      7\u001b[0m     {\n\u001b[1;32m      8\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      9\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: [{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFollowing are the frames of a video in temporal order.\u001b[39m\u001b[38;5;124m\"\u001b[39m}, \u001b[38;5;241m*\u001b[39mimage_tokens, {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m: question}]\n\u001b[1;32m     10\u001b[0m     }\n\u001b[1;32m     11\u001b[0m ]\n\u001b[1;32m     12\u001b[0m inputs \u001b[38;5;241m=\u001b[39m processor(\n\u001b[1;32m     13\u001b[0m     text\u001b[38;5;241m=\u001b[39mprocessor\u001b[38;5;241m.\u001b[39mapply_chat_template(messages, add_generation_prompt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m     14\u001b[0m     images\u001b[38;5;241m=\u001b[39mframes,\n\u001b[1;32m     15\u001b[0m     return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     16\u001b[0m )\u001b[38;5;241m.\u001b[39mto(model\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m---> 18\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_beams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.7\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdo_sample\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     20\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m processor\u001b[38;5;241m.\u001b[39mdecode(outputs[\u001b[38;5;241m0\u001b[39m], skip_special_tokens\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/generation/utils.py:2246\u001b[0m, in \u001b[0;36mGenerationMixin.generate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   2238\u001b[0m     input_ids, model_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expand_inputs_for_generation(\n\u001b[1;32m   2239\u001b[0m         input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m   2240\u001b[0m         expand_size\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mnum_beams,\n\u001b[1;32m   2241\u001b[0m         is_encoder_decoder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m   2242\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[1;32m   2243\u001b[0m     )\n\u001b[1;32m   2245\u001b[0m     \u001b[38;5;66;03m# 13. run beam sample\u001b[39;00m\n\u001b[0;32m-> 2246\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_beam_search\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2247\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeam_scorer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlogits_processor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_logits_processor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstopping_criteria\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_stopping_criteria\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgeneration_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgeneration_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2252\u001b[0m \u001b[43m        \u001b[49m\u001b[43msynced_gpus\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msynced_gpus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2254\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2256\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m generation_mode \u001b[38;5;241m==\u001b[39m GenerationMode\u001b[38;5;241m.\u001b[39mGROUP_BEAM_SEARCH:\n\u001b[1;32m   2257\u001b[0m     \u001b[38;5;66;03m# 11. prepare beam search scorer\u001b[39;00m\n\u001b[1;32m   2258\u001b[0m     beam_scorer \u001b[38;5;241m=\u001b[39m BeamSearchScorer(\n\u001b[1;32m   2259\u001b[0m         batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m   2260\u001b[0m         num_beams\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mnum_beams,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2266\u001b[0m         max_length\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mmax_length,\n\u001b[1;32m   2267\u001b[0m     )\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/generation/utils.py:3455\u001b[0m, in \u001b[0;36mGenerationMixin._beam_search\u001b[0;34m(self, input_ids, beam_scorer, logits_processor, stopping_criteria, generation_config, synced_gpus, **model_kwargs)\u001b[0m\n\u001b[1;32m   3452\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m stack_model_outputs(outputs_per_sub_batch, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget_text_config())\n\u001b[1;32m   3454\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# Unchanged original behavior\u001b[39;00m\n\u001b[0;32m-> 3455\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   3457\u001b[0m \u001b[38;5;66;03m# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping\u001b[39;00m\n\u001b[1;32m   3458\u001b[0m model_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_model_kwargs_for_generation(\n\u001b[1;32m   3459\u001b[0m     outputs,\n\u001b[1;32m   3460\u001b[0m     model_kwargs,\n\u001b[1;32m   3461\u001b[0m     is_encoder_decoder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m   3462\u001b[0m )\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:1196\u001b[0m, in \u001b[0;36mIdefics3ForConditionalGeneration.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, pixel_values, pixel_attention_mask, image_hidden_states, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1193\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[1;32m   1195\u001b[0m \u001b[38;5;66;03m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[39;00m\n\u001b[0;32m-> 1196\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1197\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1198\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1199\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1200\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1201\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1203\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpixel_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1204\u001b[0m \u001b[43m    \u001b[49m\u001b[43mimage_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1205\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1206\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1207\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1208\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1209\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1211\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1212\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlm_head(hidden_states)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:1000\u001b[0m, in \u001b[0;36mIdefics3Model.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, pixel_values, pixel_attention_mask, image_hidden_states, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    997\u001b[0m patch_attention_mask \u001b[38;5;241m=\u001b[39m (patches_subgrid\u001b[38;5;241m.\u001b[39msum(dim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mbool()\n\u001b[1;32m    999\u001b[0m \u001b[38;5;66;03m# Get sequence from the vision encoder\u001b[39;00m\n\u001b[0;32m-> 1000\u001b[0m image_hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1001\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1002\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpatch_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpatch_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1003\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mlast_hidden_state\n\u001b[1;32m   1005\u001b[0m \u001b[38;5;66;03m# Modality projection & resampling\u001b[39;00m\n\u001b[1;32m   1006\u001b[0m image_hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconnector(image_hidden_states)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:728\u001b[0m, in \u001b[0;36mIdefics3VisionTransformer.forward\u001b[0;34m(self, pixel_values, patch_attention_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    725\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_use_flash_attention_2:\n\u001b[1;32m    726\u001b[0m     patch_attention_mask \u001b[38;5;241m=\u001b[39m _prepare_4d_attention_mask(patch_attention_mask, hidden_states\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[0;32m--> 728\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    729\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    730\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpatch_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    731\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    732\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    733\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    734\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    736\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    737\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_layernorm(last_hidden_state)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:518\u001b[0m, in \u001b[0;36mIdefics3Encoder.forward\u001b[0;34m(self, inputs_embeds, attention_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    511\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    512\u001b[0m         encoder_layer\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    513\u001b[0m         hidden_states,\n\u001b[1;32m    514\u001b[0m         attention_mask,\n\u001b[1;32m    515\u001b[0m         output_attentions,\n\u001b[1;32m    516\u001b[0m     )\n\u001b[1;32m    517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 518\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mencoder_layer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    519\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    520\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    522\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    524\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    526\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:430\u001b[0m, in \u001b[0;36mIdefics3EncoderLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m    427\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[1;32m    429\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer_norm1(hidden_states)\n\u001b[0;32m--> 430\u001b[0m hidden_states, attn_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself_attn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    435\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n\u001b[1;32m    437\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/accelerate/hooks.py:170\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    168\u001b[0m         output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/transformers/models/idefics3/modeling_idefics3.py:250\u001b[0m, in \u001b[0;36mIdefics3VisionAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m    247\u001b[0m     attn_weights \u001b[38;5;241m=\u001b[39m attn_weights \u001b[38;5;241m+\u001b[39m attention_mask\n\u001b[1;32m    249\u001b[0m \u001b[38;5;66;03m# upcast attention to fp32\u001b[39;00m\n\u001b[0;32m--> 250\u001b[0m attn_weights \u001b[38;5;241m=\u001b[39m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctional\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msoftmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattn_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(query_states\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m    251\u001b[0m attn_weights \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mfunctional\u001b[38;5;241m.\u001b[39mdropout(attn_weights, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining)\n\u001b[1;32m    252\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmatmul(attn_weights, value_states)\n",
-      "File \u001b[0;32m~/Miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/functional.py:2142\u001b[0m, in \u001b[0;36msoftmax\u001b[0;34m(input, dim, _stacklevel, dtype)\u001b[0m\n\u001b[1;32m   2140\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39msoftmax(dim)\n\u001b[1;32m   2141\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2142\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msoftmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
-      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 3.96 GiB. GPU 0 has a total capacity of 11.75 GiB of which 3.77 GiB is free. Including non-PyTorch memory, this process has 6.59 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1.31 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
+      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
      ]
     }
    ],
@@ -431,6 +420,11 @@
     "# print(\"Question:\", question)\n",
     "print(\"Response:\", response)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {

From f71ede6c1878b42d82d1d9f9364e341b1dd5851d Mon Sep 17 00:00:00 2001
From: duydl <duy.dole.00ece@gmail.com>
Date: Sun, 22 Dec 2024 15:19:26 +0700
Subject: [PATCH 15/63] Fix typo. Add more explanation

---
 .../notebooks/vlm_usage_sample.ipynb          | 55 +++++++++++--------
 1 file changed, 32 insertions(+), 23 deletions(-)

diff --git a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
index 8cd2b85d..04f1c86b 100644
--- a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -6,31 +6,19 @@
    "source": [
     "# Processing images and text with VLMs \n",
     "\n",
-    "This notebook demonstrates how to utilize the `HuggingFaceTB/SmolVLM-Instruct` 4bit-quantized model for various multimodal tasks.\n",
+    "This notebook demonstrates how to utilize the `HuggingFaceTB/SmolVLM-Instruct` 4bit-quantized model for various multimodal tasks such as:\n",
+    "- Visual Question Answering (VQA): Answering questions based on image content.\n",
+    "- Text Recognition (OCR): Extracting and interpreting text in images.\n",
+    "- Video Understanding: Describing videos through sequential frame analysis.\n",
     "\n",
-    "generating image captions, answering questions based on visuals, or understanding the relationship between textual and visual data. object detection"
+    "By structuring prompts effectively, you can leverage the model for many applications, such as scene understanding, document analysis, and dynamic visual reasoning."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd19d9e0d17b4e349f1d60a4fb5c79a4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Install the requirements in Google Colab\n",
     "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
@@ -95,7 +83,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let try question answering on some image. "
+    "Let's start with generating captions and answering questions about an image. We'll also explore processing multiple images.\n",
+    "### 1. Single Image Captioning"
    ]
   },
   {
@@ -193,7 +182,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We could also input multiple image. "
+    "### 2. Comparing Multiple Images\n",
+    "The model can process and compare multiple images. Let's determine the common theme between two images."
    ]
   },
   {
@@ -253,7 +243,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "VLM also have great OCR capability. Try experiment with image with denser text."
+    "### 🔠 Text Recognition (OCR)\n",
+    "VLM can also recognize and interpret text in images, making it suitable for tasks like document analysis.\n",
+    "You could try experimenting on images with denser text."
    ]
   },
   {
@@ -320,6 +312,12 @@
    "source": [
     "## Processing videos\n",
     "\n",
+    "Visual-Language Models (VLMs) can process videos indirectly by extracting keyframes and reasoning over them in temporal order. While VLMs lack the temporal modeling capabilities of dedicated video models, they can still:\n",
+    "- Describe actions or events by analyzing sampled frames sequentially.\n",
+    "- Answer questions about videos based on representative keyframes.\n",
+    "- Summarize video content by combining textual descriptions of multiple frames.\n",
+    "\n",
+    "Let experiment on an example:\n",
     "\n",
     "<video width=\"640\" height=\"360\" controls>\n",
     "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
@@ -424,7 +422,18 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "## 💐 You're Done!\n",
+    "\n",
+    "This notebook demonstrated how to use a Visual-Language Model (VLM) such as formating prompts for multimodal tasks. By following the steps outlined here, you can experiment with VLMs and their applications.\n",
+    "\n",
+    "### Next Steps to Explore:\n",
+    "- Experiment with more use cases of VLMs.\n",
+    "- Collaborate with a colleague by reviewing their pull requests (PRs).\n",
+    "- Contribute to improving this course material by opening an issue or submitting a PR to introduce new use cases, examples, or concepts.\n",
+    "\n",
+    "Happy exploring! 🌟"
+   ]
   }
  ],
  "metadata": {

From bb4915d3a6c190c30ace4c3ac06995a413f9baf7 Mon Sep 17 00:00:00 2001
From: duydl <duy.dole.00ece@gmail.com>
Date: Sun, 22 Dec 2024 15:23:07 +0700
Subject: [PATCH 16/63] Add deps

---
 .../notebooks/vlm_usage_sample.ipynb                     | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
index 04f1c86b..59f29d42 100644
--- a/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ b/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -325,6 +325,15 @@
     "</video>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install opencv-python"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,

From 6352161a0b55330236e0f9bd6f071f41050c3e74 Mon Sep 17 00:00:00 2001
From: duydl <duy.dole.00ece@gmail.com>
Date: Sun, 22 Dec 2024 15:25:17 +0700
Subject: [PATCH 17/63] Fix typo

---
 .../notebooks/vlm_sft_sample.ipynb            | 40 ++++---------------
 1 file changed, 8 insertions(+), 32 deletions(-)

diff --git a/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
index b37bc534..b4303d42 100644
--- a/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
+++ b/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
@@ -12,32 +12,17 @@
     "    <h2 style='margin: 0;color:blue'>Exercise: Fine-Tuning SmolVLM with SFTTrainer</h2>\n",
     "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
     "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Use the `\"HuggingFaceM4/ChartQA\"` dataset for SFT training run.</p>\n",
-    "    <p>🐕 Use the fine-tuned to model generate a response, and improve upon the base example..</p>\n",
+    "    <p>🐢 Use the `HuggingFaceM4/ChartQA` dataset for SFT training run.</p>\n",
+    "    <p>🐕 Use the fine-tuned to model generate a response, and improve upon the base example.</p>\n",
     "    <p>🦁 Try out the other datasets and show improvement.</p>\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a374dfc7aad431cb5d580a7f3657d6d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Install the requirements in Google Colab\n",
     "# !pip install transformers datasets trl huggingface_hub\n",
@@ -347,18 +332,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:309: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from trl import SFTConfig, SFTTrainer\n",
     "\n",
@@ -453,7 +429,7 @@
    "source": [
     "## 💐 You're done!\n",
     "\n",
-    "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolVLM` model using the `Trainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n",
+    "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolVLM` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n",
     "\n",
     "- Try this notebook on a harder difficulty\n",
     "- Review a colleagues PR\n",
@@ -477,7 +453,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,

From 9a29a3770a0d14092bd31803c9bc0c19c01ea390 Mon Sep 17 00:00:00 2001
From: duydl <duy.dole.00ece@gmail.com>
Date: Sun, 22 Dec 2024 15:26:57 +0700
Subject: [PATCH 18/63] Typo

---
 5_vision_language_models/notebooks/vlm_sft_sample.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
index b4303d42..5af7d6a2 100644
--- a/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
+++ b/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Fine-Tuning a VLM \n",
     "\n",
-    "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolVLM-Instruct` model using the `Trainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n",
+    "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolVLM-Instruct` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
     "    <h2 style='margin: 0;color:blue'>Exercise: Fine-Tuning SmolVLM with SFTTrainer</h2>\n",

From bc89a278f3b97a0cb7876d00792b206a6e1693da Mon Sep 17 00:00:00 2001
From: Khanh Thanh <100078354+khanhthanhdev@users.noreply.github.com>
Date: Tue, 24 Dec 2024 08:26:13 +0000
Subject: [PATCH 19/63] add chapter 3

---
 vi/3_parameter_efficient_finetuning/README.md |   39 +
 .../images/lora_adapter.png                   |  Bin 0 -> 1239572 bytes
 .../lora_adapters.md                          |  126 +
 .../notebooks/finetune_sft_peft.ipynb         |  516 ++
 .../notebooks/load_lora_adapter.ipynb         | 5061 +++++++++++++++++
 .../prompt_tuning.md                          |   74 +
 6 files changed, 5816 insertions(+)
 create mode 100644 vi/3_parameter_efficient_finetuning/README.md
 create mode 100644 vi/3_parameter_efficient_finetuning/images/lora_adapter.png
 create mode 100644 vi/3_parameter_efficient_finetuning/lora_adapters.md
 create mode 100644 vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
 create mode 100644 vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
 create mode 100644 vi/3_parameter_efficient_finetuning/prompt_tuning.md

diff --git a/vi/3_parameter_efficient_finetuning/README.md b/vi/3_parameter_efficient_finetuning/README.md
new file mode 100644
index 00000000..5bc04185
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/README.md
@@ -0,0 +1,39 @@
+# Parameter-Efficient Fine-Tuning (PEFT)
+
+Khi các mô hình ngôn ngữ ngày càng lớn, việc tinh chỉnh theo cách truyền thống trở nên ngày càng khó khăn. Việc cập nhật toàn bộ một mô hình với hàng tỷ tham số đòi hỏi một lượng lớn bộ nhớ GPU, tốn kém chi phí lưu trữ các phiên bản tinh chỉnh, và thậm chí có thể làm giảm khả năng khái quát hóa ban đầu của mô hình. Phương pháp Tinh chỉnh Hiệu quả Tham số (Parameter-Efficient Fine-Tuning - PEFT) xuất hiện như một giải pháp nhằm giải quyết các thách thức này, bằng cách chỉ điều chỉnh một tập hợp nhỏ tham số của mô hình trong khi giữ nguyên phần lớn tham số ban đầu.
+
+Trong khi tinh chỉnh truyền thống đòi hỏi phải cập nhật toàn bộ tham số của mô hình trong quá trình huấn luyện, các phương pháp PEFT chỉ yêu cầu điều chỉnh dưới 1% số tham số của mô hình gốc. Cách tiếp cận này mang lại những lợi ích vượt trội:
+
+- Cho phép fine-tuning trên phần cứng phổ thông với bộ nhớ GPU hạn chế.
+- Tối ưu hóa việc lưu trữ, dễ dàng quản lý nhiều mô hình thích ứng cho từng tác vụ cụ thể.
+- Cải thiện khả năng tổng quát hóa trong các tình huống dữ liệu hạn chế.
+- Rút ngắn đáng kể thời gian huấn luyện và đánh giá.
+
+## Các phương pháp phổ biến
+
+In this module, we will cover two popular PEFT methods:
+
+### 1️⃣ LoRA (Low-Rank Adaptation)
+
+LoRA đã trở thành phương pháp PEFT được áp dụng rộng rãi nhất, mang lại một giải pháp tinh tế cho việc thích nghi mô hình hiệu quả. Thay vì chỉnh sửa toàn bộ mô hình, **LoRA chèn thêm các ma trận có thể huấn luyện vào các lớp attention của mô hình**. Phương pháp này thường giúp giảm khoảng 90% số lượng tham số cần huấn luyện, đồng thời vẫn duy trì hiệu suất tương đương với việc fine-tuning toàn bộ mô hình. Chúng ta sẽ tìm hiểu chi tiết về LoRA trong mục [LoRA (Low-Rank Adaptation)](./lora_adapters.md).
+ 
+### 2️⃣ Tinh chỉnh Prompt
+
+Prompt tuning cung cấp một cách tiếp cận **nhẹ nhàng** hơn nữa bằng cách **thêm các token có thể huấn luyện** vào đầu vào thay vì chỉnh sửa trọng số của mô hình. Mặc dù prompt tuning ít phổ biến hơn so với LoRA, nhưng đây vẫn là một kỹ thuật hữu ích để nhanh chóng thích nghi mô hình với các nhiệm vụ hoặc lĩnh vực mới. Chúng ta sẽ khám phá prompt tuning trong mục [Prompt Tuning](./prompt_tuning.md) 
+
+## Bài tập
+
+| Tiêu đề | Mô tả | Bài tập | Đường dẫn | Colab |
+|-------|-------------|----------|------|-------|
+| LoRA Fine-tuning | Tìm hiểu cách Fine-tune mô hình sử dụng LoRA | 🐢 Huấn luyện mô hình sử dụng LoRA<br>🐕 Thử nghiệm với nhiều giá trị hạng khác nhau<br>🦁 So sánh hiệu quả với fine-tune toàn bộ | [Notebook](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |  
+| Load LoRA Adapters | Tìm hiểu cách tải và sử dụng các LoRA adapter đã được huấn luyện | 🐢 Tải adapter đã được huấn luyện trước<br>🐕 Gộp adapter với mô hình gốc<br>🦁 Chuyển đổi giữa nhiều adapter | [Notebook](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |  
+<!-- | Prompt Tuning | Learn how to implement prompt tuning | 🐢 Train soft prompts<br>🐕 Compare different initialization strategies<br>🦁 Evaluate on multiple tasks | [Notebook](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
+
+## Resources
+- [PEFT Documentation](https://huggingface.co/docs/peft)
+- [LoRA Paper](https://arxiv.org/abs/2106.09685)
+- [QLoRA Paper](https://arxiv.org/abs/2305.14314)
+- [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691)
+- [Hugging Face PEFT Guide](https://huggingface.co/blog/peft)
+- [How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
+- [TRL](https://huggingface.co/docs/trl/index)
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diff --git a/vi/3_parameter_efficient_finetuning/lora_adapters.md b/vi/3_parameter_efficient_finetuning/lora_adapters.md
new file mode 100644
index 00000000..ee3358ac
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+++ b/vi/3_parameter_efficient_finetuning/lora_adapters.md
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+# LoRA
+
+LoRA đã trở thành phương pháp PEFT được áp dụng rộng rãi nhất. Nó hoạt động bằng cách thêm các ma trận phân rã hạng nhỏ vào trọng số attention, thường giảm khoảng 90% tham số cần huấn luyện.
+
+## Hiểu về LoRA
+
+LoRA là một kỹ thuật tinh chỉnh hiệu quả về tham số, đóng băng các trọng số của mô hình đã huấn luyện trước và đưa các ma trận phân rã hạng có thể huấn luyện vào các lớp của mô hình. Thay vì huấn luyện tất cả tham số mô hình trong quá trình tinh chỉnh, LoRA phân rã các cập nhật trọng số thành các ma trận nhỏ hơn thông qua phân rã hạng thấp, giảm đáng kể số lượng tham số cần huấn luyện trong khi vẫn duy trì hiệu suất mô hình. Ví dụ, khi áp dụng cho GPT-3 175B, LoRA đã giảm số tham số cần huấn luyện xuống 10.000 lần và yêu cầu bộ nhớ GPU xuống 3 lần so với tinh chỉnh toàn bộ. Bạn có thể đọc thêm về LoRA trong [bài báo LoRA](https://arxiv.org/pdf/2106.09685).
+
+LoRA hoạt động bằng cách thêm các cặp ma trận phân rã hạng vào các lớp transformer, thường tập trung vào trọng số attention. Trong quá trình suy luận, các trọng số bộ điều hợp này có thể được kết hợp với mô hình cơ sở, không gây thêm độ trễ. LoRA đặc biệt hữu ích để điều chỉnh các mô hình ngôn ngữ lớn cho các nhiệm vụ hoặc lĩnh vực cụ thể trong khi vẫn giữ các yêu cầu tài nguyên ở mức quản lý được.
+
+## Tải LoRA Adapters
+
+Các bộ điều hợp có thể được tải vào mô hình đã huấn luyện trước với load_adapter(), điều này hữu ích khi thử nghiệm các bộ điều hợp khác nhau mà trọng số chưa được kết hợp. Thiết lập trọng số bộ điều hợp đang hoạt động với hàm set_adapter(). Để quay lại mô hình cơ sở, bạn có thể sử dụng unload() để dỡ tất cả các module LoRA. Điều này giúp dễ dàng chuyển đổi giữa các trọng số cho các nhiệm vụ cụ thể khác nhau.
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
+peft_model_id = "<peft_adapter_id>"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+```
+
+![lora_load_adapter](./images/lora_adapter.png)
+
+## Kết hợp LoRA Adapters
+
+Sau khi huấn luyện với LoRA, bạn có thể muốn kết hợp trọng số bộ điều hợp trở lại mô hình cơ sở để triển khai dễ dàng hơn. Điều này tạo ra một mô hình duy nhất với trọng số kết hợp, loại bỏ nhu cầu tải bộ điều hợp riêng biệt trong quá trình suy luận.
+
+Quá trình kết hợp đòi hỏi chú ý đến quản lý bộ nhớ và độ chính xác. Vì bạn sẽ cần tải cả mô hình cơ sở và trọng số bộ điều hợp cùng lúc, hãy đảm bảo có đủ bộ nhớ GPU/CPU. Sử dụng `device_map="auto"` trong `transformers` sẽ giúp quản lý bộ nhớ tự động. Duy trì độ chính xác nhất quán (ví dụ: float16) trong suốt quá trình, khớp với độ chính xác được sử dụng trong quá trình huấn luyện và lưu mô hình đã kết hợp ở cùng định dạng để triển khai. Trước khi triển khai, luôn xác nhận mô hình đã kết hợp bằng cách so sánh đầu ra và các chỉ số hiệu suất với phiên bản dựa trên bộ điều hợp.
+
+Các bộ điều hợp cũng thuận tiện để chuyển đổi giữa các nhiệm vụ hoặc lĩnh vực khác nhau. Bạn có thể tải mô hình cơ sở và trọng số bộ điều hợp riêng biệt. Điều này cho phép chuyển đổi nhanh chóng giữa các trọng số cho nhiệm vụ cụ thể khác nhau.
+
+## Hướng dẫn triển khai
+
+Thư mục `notebooks/` chứa các hướng dẫn thực hành và bài tập để triển khai các phương pháp PEFT khác nhau. Bắt đầu với `load_lora_adapter_example.ipynb` để có giới thiệu cơ bản, sau đó khám phá `lora_finetuning.ipynb` để xem xét chi tiết hơn về cách tinh chỉnh mô hình với LoRA và SFT.
+
+Khi triển khai các phương pháp PEFT, bắt đầu với các giá trị hạng nhỏ (4-8) cho LoRA và theo dõi độ mất trong huấn luyện. Sử dụng tập kiểm định để ngăn chặn overfitting và so sánh kết quả với các đường cơ sở tinh chỉnh toàn bộ khi có thể. Hiệu quả của các phương pháp khác nhau có thể thay đổi theo nhiệm vụ, vì vậy thực nghiệm là chìa khóa.
+
+## OLoRA
+
+[OLoRA](https://arxiv.org/abs/2406.01775) sử dụng phân rã QR để khởi tạo bộ điều hợp LoRA. OLoRA dịch chuyển trọng số cơ sở của mô hình theo hệ số phân rã QR của chúng, nghĩa là nó thay đổi trọng số trước khi thực hiện bất kỳ huấn luyện nào trên chúng. Cách tiếp cận này cải thiện đáng kể tính ổn định, tăng tốc độ hội tụ và cuối cùng đạt được hiệu suất vượt trội.
+
+## Sử dụng TRL với PEFT
+
+Các phương pháp PEFT có thể được kết hợp với TRL (Transformers Reinforcement Learning) để tinh chỉnh hiệu quả. Sự tích hợp này đặc biệt hữu ích cho RLHF (Reinforcement Learning from Human Feedback) vì nó giảm yêu cầu bộ nhớ.
+
+```python
+from peft import LoraConfig
+from transformers import AutoModelForCausalLM
+
+# Tải mô hình với cấu hình PEFT
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# Tải mô hình trên thiết bị cụ thể
+model = AutoModelForCausalLM.from_pretrained(
+    "your-model-name",
+    load_in_8bit=True,  # Tùy chọn: sử dụng 8-bit
+    device_map="auto",
+    peft_config=lora_config
+)
+```
+
+Ở trên, chúng ta đã sử dụng `device_map="auto"` để tự động gán mô hình cho thiết bị phù hợp. Bạn cũng có thể gán thủ công mô hình cho một thiết bị cụ thể bằng cách sử dụng `device_map={"": device_index}`. Bạn cũng có thể mở rộng việc huấn luyện trên nhiều GPU trong khi vẫn giữ việc sử dụng bộ nhớ hiệu quả.
+
+## Triển khai kết hợp cơ bản
+
+Sau khi huấn luyện bộ điều hợp LoRA, bạn có thể kết hợp trọng số bộ điều hợp trở lại mô hình cơ sở. Đây là cách thực hiện:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+# 1. Tải mô hình cơ sở
+base_model = AutoModelForCausalLM.from_pretrained(
+    "base_model_name",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# 2. Tải mô hình PEFT với bộ điều hợp
+peft_model = PeftModel.from_pretrained(
+    base_model,
+    "path/to/adapter",
+    torch_dtype=torch.float16
+)
+
+# 3. Kết hợp trọng số bộ điều hợp với mô hình cơ sở
+try:
+    merged_model = peft_model.merge_and_unload()
+except RuntimeError as e:
+    print(f"Kết hợp thất bại: {e}")
+    # Triển khai dự phòng hoặc tối ưu hóa bộ nhớ
+
+# 4. Lưu mô hình đã kết hợp
+merged_model.save_pretrained("path/to/save/merged_model")
+```
+
+Nếu bạn gặp sự khác biệt về kích thước trong mô hình đã lưu, hãy đảm bảo bạn cũng lưu tokenizer:
+
+```python
+# Lưu cả mô hình và tokenizer
+tokenizer = AutoTokenizer.from_pretrained("base_model_name")
+merged_model.save_pretrained("path/to/save/merged_model")
+tokenizer.save_pretrained("path/to/save/merged_model")
+```
+
+## Các bước tiếp theo
+
+⏩ Chuyển đến [Hướng dẫn Prompt Tuning](prompt_tuning.md) để tìm hiểu cách tinh chỉnh mô hình với prompt tuning.
+⏩ Chuyển đến [Hướng dẫn tải bộ điều hợp LoRA](./notebooks/load_lora_adapter.ipynb) để tìm hiểu cách tải các bộ điều hợp LoRA.
+
+# Tài nguyên
+
+- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
+- [Tài liệu PEFT](https://huggingface.co/docs/peft)
+- [Bài viết blog Hugging Face về PEFT](https://huggingface.co/blog/peft)
+
+
diff --git a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
new file mode 100644
index 00000000..0082900b
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -0,0 +1,516 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z-6LLOPZouLg"
+   },
+   "source": [
+    "# Cách tinh chỉnh mô hình LLM với bộ điều hợp LoRA sử dụng Hugging Face TRL\n",
+    "\n",
+    "Notebook này trình bày cách tinh chỉnh hiệu quả các mô hình ngôn ngữ lớn sử dụng bộ điều hợp LoRA (Thích nghi hạng thấp). LoRA là một kỹ thuật tinh chỉnh hiệu quả về tham số với các đặc điểm:\n",
+    "- Đóng băng trọng số mô hình đã huấn luyện trước\n",
+    "- Thêm các ma trận phân rã hạng nhỏ có thể huấn luyện vào các lớp attention\n",
+    "- Thường giảm khoảng 90% số tham số cần huấn luyện\n",
+    "- Duy trì hiệu suất mô hình trong khi vẫn tiết kiệm bộ nhớ\n",
+    "\n",
+    "Chúng ta sẽ tìm hiểu:\n",
+    "1. Thiết lập môi trường phát triển và cấu hình LoRA\n",
+    "2. Tạo và chuẩn bị tập dữ liệu để huấn luyện bộ điều hợp\n",
+    "3. Tinh chỉnh sử dụng `trl` và `SFTTrainer` với bộ điều hợp LoRA\n",
+    "4. Kiểm thử mô hình và kết hợp bộ điều hợp (tùy chọn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fXqd9BXgouLi"
+   },
+   "source": [
+    "## 1. Thiết lập môi trường \n",
+    "\n",
+    "Bước đầu tiên là cài đặt Thư viện Hugging Face và Pyroch, bao gồm trl, transformers và datasets. Nếu bạn chưa nghe nói về trl, đừng lo lắng. Đây là một thư viện mới trên đầu transformers và datasets, giúp tinh chỉnh các LLM mở dễ dàng hơn.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tKvGVxImouLi"
+   },
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XHUzfwpKouLk"
+   },
+   "source": [
+    "## 2. Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 2260\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 119\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load a sample dataset\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: define your dataset and config using the path and name parameters\n",
+    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TOhJdtsouLk"
+   },
+   "source": [
+    "## 3. Fine-tune LLM using `trl` and the `SFTTrainer` with LoRA\n",
+    "\n",
+    "The [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) from `trl` provides integration with LoRA adapters through the [PEFT](https://huggingface.co/docs/peft/en/index) library. Key advantages of this setup include:\n",
+    "\n",
+    "1. **Memory Efficiency**: \n",
+    "   - Only adapter parameters are stored in GPU memory\n",
+    "   - Base model weights remain frozen and can be loaded in lower precision\n",
+    "   - Enables fine-tuning of large models on consumer GPUs\n",
+    "\n",
+    "2. **Training Features**:\n",
+    "   - Native PEFT/LoRA integration with minimal setup\n",
+    "   - Support for QLoRA (Quantized LoRA) for even better memory efficiency\n",
+    "\n",
+    "3. **Adapter Management**:\n",
+    "   - Adapter weight saving during checkpoints\n",
+    "   - Features to merge adapters back into base model\n",
+    "\n",
+    "We'll use LoRA in our example, which combines LoRA with 4-bit quantization to further reduce memory usage without sacrificing performance. The setup requires just a few configuration steps:\n",
+    "1. Define the LoRA configuration (rank, alpha, dropout)\n",
+    "2. Create the SFTTrainer with PEFT config\n",
+    "3. Train and save the adapter weights\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# Load the model and tokenizer\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# Set up the chat format\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZbuVArTHouLk"
+   },
+   "source": [
+    "The `SFTTrainer`  supports a native integration with `peft`, which makes it super easy to efficiently tune LLMs using, e.g. LoRA. We only need to create our `LoraConfig` and provide it to the trainer.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercise: Define LoRA parameters for finetuning</h2>\n",
+    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
+    "    <p><b>Difficulty Levels</b></p>\n",
+    "    <p>🐢 Use the general parameters for an abitrary finetune</p>\n",
+    "    <p>🐕 Adjust the parameters and review in weights & biases.</p>\n",
+    "    <p>🦁 Adjust the parameters and show change in inference results.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "blDSs9swouLk"
+   },
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig\n",
+    "\n",
+    "# TODO: Configure LoRA parameters\n",
+    "# r: rank dimension for LoRA update matrices (smaller = more compression)\n",
+    "rank_dimension = 6\n",
+    "# lora_alpha: scaling factor for LoRA layers (higher = stronger adaptation)\n",
+    "lora_alpha = 8\n",
+    "# lora_dropout: dropout probability for LoRA layers (helps prevent overfitting)\n",
+    "lora_dropout = 0.05\n",
+    "\n",
+    "peft_config = LoraConfig(\n",
+    "    r=rank_dimension,  # Rank dimension - typically between 4-32\n",
+    "    lora_alpha=lora_alpha,  # LoRA scaling factor - typically 2x rank\n",
+    "    lora_dropout=lora_dropout,  # Dropout probability for LoRA layers\n",
+    "    bias=\"none\",  # Bias type for LoRA. the corresponding biases will be updated during training.\n",
+    "    target_modules=\"all-linear\",  # Which modules to apply LoRA to\n",
+    "    task_type=\"CAUSAL_LM\",  # Task type for model architecture\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l5NUDPcaouLl"
+   },
+   "source": [
+    "Before we can start our training we need to define the hyperparameters (`TrainingArguments`) we want to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NqT28VZlouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# Training configuration\n",
+    "# Hyperparameters based on QLoRA paper recommendations\n",
+    "args = SFTConfig(\n",
+    "    # Output settings\n",
+    "    output_dir=finetune_name,  # Directory to save model checkpoints\n",
+    "    # Training duration\n",
+    "    num_train_epochs=1,  # Number of training epochs\n",
+    "    # Batch size settings\n",
+    "    per_device_train_batch_size=2,  # Batch size per GPU\n",
+    "    gradient_accumulation_steps=2,  # Accumulate gradients for larger effective batch\n",
+    "    # Memory optimization\n",
+    "    gradient_checkpointing=True,  # Trade compute for memory savings\n",
+    "    # Optimizer settings\n",
+    "    optim=\"adamw_torch_fused\",  # Use fused AdamW for efficiency\n",
+    "    learning_rate=2e-4,  # Learning rate (QLoRA paper)\n",
+    "    max_grad_norm=0.3,  # Gradient clipping threshold\n",
+    "    # Learning rate schedule\n",
+    "    warmup_ratio=0.03,  # Portion of steps for warmup\n",
+    "    lr_scheduler_type=\"constant\",  # Keep learning rate constant after warmup\n",
+    "    # Logging and saving\n",
+    "    logging_steps=10,  # Log metrics every N steps\n",
+    "    save_strategy=\"epoch\",  # Save checkpoint every epoch\n",
+    "    # Precision settings\n",
+    "    bf16=True,  # Use bfloat16 precision\n",
+    "    # Integration settings\n",
+    "    push_to_hub=False,  # Don't push to HuggingFace Hub\n",
+    "    report_to=None,  # Disable external logging\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cGhR7uFBouLl"
+   },
+   "source": [
+    "We now have every building block we need to create our `SFTTrainer` to start then training our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "M00Har2douLl"
+   },
+   "outputs": [],
+   "source": [
+    "max_seq_length = 1512  # max sequence length for model and packing of the dataset\n",
+    "\n",
+    "# Create SFTTrainer with LoRA configuration\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    peft_config=peft_config,  # LoRA configuration\n",
+    "    max_seq_length=max_seq_length,  # Maximum sequence length\n",
+    "    tokenizer=tokenizer,\n",
+    "    packing=True,  # Enable input packing for efficiency\n",
+    "    dataset_kwargs={\n",
+    "        \"add_special_tokens\": False,  # Special tokens handled by template\n",
+    "        \"append_concat_token\": False,  # No additional separator needed\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zQ_kRN24ouLl"
+   },
+   "source": [
+    "Start training our model by calling the `train()` method on our `Trainer` instance. This will start the training loop and train our model for 3 epochs. Since we are using a PEFT method, we will only save the adapted model weights and not the full model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Tq4nIYqKouLl"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "300e5dfbb4b54750b77324345c7591f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/72 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# start training, the model will be automatically saved to the hub and the output directory\n",
+    "trainer.train()\n",
+    "\n",
+    "# save model\n",
+    "trainer.save_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y4HHSYYzouLl"
+   },
+   "source": [
+    "The training with Flash Attention for 3 epochs with a dataset of 15k samples took 4:14:36 on a `g5.2xlarge`. The instance costs `1.21$/h` which brings us to a total cost of only ~`5.3$`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C309KsXjouLl"
+   },
+   "source": [
+    "### Merge LoRA Adapter into the Original Model\n",
+    "\n",
+    "When using LoRA, we only train adapter weights while keeping the base model frozen. During training, we save only these lightweight adapter weights (~2-10MB) rather than a full model copy. However, for deployment, you might want to merge the adapters back into the base model for:\n",
+    "\n",
+    "1. **Simplified Deployment**: Single model file instead of base model + adapters\n",
+    "2. **Inference Speed**: No adapter computation overhead\n",
+    "3. **Framework Compatibility**: Better compatibility with serving frameworks\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import AutoPeftModelForCausalLM\n",
+    "\n",
+    "\n",
+    "# Load PEFT model on CPU\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=args.output_dir,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    low_cpu_mem_usage=True,\n",
+    ")\n",
+    "\n",
+    "# Merge LoRA and base model and save\n",
+    "merged_model = model.merge_and_unload()\n",
+    "merged_model.save_pretrained(\n",
+    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yO6E9quouLl"
+   },
+   "source": [
+    "## 3. Test Model and run Inference\n",
+    "\n",
+    "After the training is done we want to test our model. We will load different samples from the original dataset and evaluate the model on those samples, using a simple loop and accuracy as our metric.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Bonus Exercise: Load LoRA Adapter</h2>\n",
+    "    <p>Use what you learnt from the ecample note book to load your trained LoRA adapter for inference.</p> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "I5B494OdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# free the memory again\n",
+    "del model\n",
+    "del trainer\n",
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "P1UhohVdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from peft import AutoPeftModelForCausalLM\n",
+    "from transformers import AutoTokenizer, pipeline\n",
+    "\n",
+    "# Load Model with PEFT adapter\n",
+    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
+    ")\n",
+    "pipe = pipeline(\n",
+    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "99uFDAuuouLl"
+   },
+   "source": [
+    "Lets test some prompt samples and see how the model performs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "-shSmUbvouLl",
+    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
+   },
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n",
+    "    \"Write a Python function to calculate the factorial of a number.\",\n",
+    "    \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n",
+    "    \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def test_inference(prompt):\n",
+    "    prompt = pipe.tokenizer.apply_chat_template(\n",
+    "        [{\"role\": \"user\", \"content\": prompt}],\n",
+    "        tokenize=False,\n",
+    "        add_generation_prompt=True,\n",
+    "    )\n",
+    "    outputs = pipe(\n",
+    "        prompt,\n",
+    "    )\n",
+    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
+    "\n",
+    "\n",
+    "for prompt in prompts:\n",
+    "    print(f\"    prompt:\\n{prompt}\")\n",
+    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
+    "    print(\"-\" * 50)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
new file mode 100644
index 00000000..b288a4a1
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
@@ -0,0 +1,5061 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "otj46qRbtpnd",
+        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q bitsandbytes datasets accelerate loralib\n",
+        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 446,
+          "referenced_widgets": [
+            "a65941572c1845afb75e39553d89a217",
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+        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import bitsandbytes as bnb\n",
+        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S65GcxNGA9kz"
+      },
+      "source": [
+        "## Load adapters from the Hub\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517,
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+        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import transformers\n",
+        "from datasets import load_dataset\n",
+        "from peft import LoraConfig, get_peft_model\n",
+        "from peft import PeftModel, PeftConfig\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "\n",
+        "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n",
+        "config = PeftConfig.from_pretrained(peft_model_id)\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    config.base_model_name_or_path,\n",
+        "    return_dict=True,\n",
+        "    load_in_8bit=True,\n",
+        "    device_map=\"auto\",\n",
+        ")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+        "\n",
+        "# Load the Lora model\n",
+        "model = PeftModel.from_pretrained(model, peft_model_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MDqJWba-tpnv",
+        "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf"
+      },
+      "outputs": [],
+      "source": [
+        "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n",
+        "\n",
+        "with torch.cuda.amp.autocast():\n",
+        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
+        "\n",
+        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
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+        "name": "ipython",
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diff --git a/vi/3_parameter_efficient_finetuning/prompt_tuning.md b/vi/3_parameter_efficient_finetuning/prompt_tuning.md
new file mode 100644
index 00000000..cdde8dea
--- /dev/null
+++ b/vi/3_parameter_efficient_finetuning/prompt_tuning.md
@@ -0,0 +1,74 @@
+# Prompt Tuning
+
+Kỹ thuật tinh chỉnh prompt là một cách tiếp cận hiệu quả về mặt tham số, tập trung vào việc điều chỉnh cách biểu diễn dữ liệu đầu vào thay vì thay đổi trọng số của mô hình. Thay vì cập nhật toàn bộ tham số như cách tinh chỉnh truyền thống, tinh chỉnh prompt chỉ thêm vào và tối ưu một số ít token có thể huấn luyện trong khi vẫn giữ nguyên mô hình gốc.
+
+## Understanding Prompt Tuning
+
+Điều chỉnh prompt (prompt tuning) là một phương pháp thay thế hiệu quả về thông số so với việc tinh chỉnh toàn bộ mô hình, bằng cách thêm các vector liên tục có thể huấn luyện (prompt mềm) vào phần đầu của văn bản đầu vào. Khác với các prompt văn bản rời rạc, những prompt mềm này được học thông qua lan truyền ngược (backpropagation) trong khi giữ nguyên mô hình ngôn ngữ. Phương pháp này được giới thiệu trong nghiên cứu [Sức mạnh của quy mô trong việc điều chỉnh prompt hiệu quả về thông số](https://arxiv.org/abs/2104.08691) (Lester và cộng sự, 2021), chứng minh rằng việc điều chỉnh prompt trở nên cạnh tranh hơn với phương pháp tinh chỉnh mô hình khi kích thước mô hình tăng lên. Trong bài báo, khi mô hình đạt khoảng 10 tỷ tham số, hiệu suất của việc điều chỉnh prompt ngang bằng với việc tinh chỉnh mô hình, trong khi chỉ cần điều chỉnh vài trăm thông số cho mỗi tác vụ.
+
+Những prompt mềm này là các vector liên tục trong không gian nhúng của mô hình và được tối ưu hóa trong quá trình huấn luyện. Khác với các prompt rời rạc truyền thống sử dụng các token ngôn ngữ tự nhiên, các prompt mềm không mang ý nghĩa vốn có nhưng học cách kích hoạt hành vi mong muốn từ mô hình đã đóng băng thông qua phương pháp gradient descent. Kỹ thuật này đặc biệt hiệu quả cho các tình huống đa nhiệm vụ, bởi mỗi nhiệm vụ chỉ cần lưu trữ một vector prompt nhỏ (thường là vài trăm thông số) thay vì một bản sao đầy đủ của mô hình. Cách tiếp cận này không chỉ duy trì được dung lượng bộ nhớ tối thiểu mà còn cho phép chuyển đổi nhiệm vụ nhanh chóng bằng cách đơn giản là hoán đổi các vector prompt mà không cần tải lại mô hình.
+
+## Training Process
+
+Các prompt mềm thường có số lượng từ 8 đến 32 token và có thể được khởi tạo ngẫu nhiên hoặc từ văn bản có sẵn. Phương pháp khởi tạo đóng vai trò quan trọng trong quá trình huấn luyện, trong đó việc khởi tạo dựa trên văn bản thường mang lại hiệu quả tốt hơn so với khởi tạo ngẫu nhiên.
+
+Trong quá trình huấn luyện, chỉ các thông số của prompt được cập nhật trong khi mô hình cơ sở vẫn giữ nguyên không đổi. Cách tiếp cận tập trung này sử dụng các mục tiêu huấn luyện tiêu chuẩn nhưng đòi hỏi phải chú ý kỹ lưỡng đến tốc độ học và hành vi gradient của các token prompt.
+
+## Implementation with PEFT
+
+Thư viện PEFT giúp việc triển khai tinh chỉnh prompt trở nên đơn giản. Đây là một ví dụ cơ bản:
+
+```python
+from peft import PromptTuningConfig, TaskType, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Tải model ban đầu
+model = AutoModelForCausalLM.from_pretrained("your-base-model")
+tokenizer = AutoTokenizer.from_pretrained("your-base-model")
+
+# Cài đặt tinh chỉnh prompt
+peft_config = PromptTuningConfig(
+    task_type=TaskType.CAUSAL_LM,
+    num_virtual_tokens=8,  # Số lượng token huấn luyện
+    prompt_tuning_init="TEXT",  # Khởi tạo từ dạng text
+    prompt_tuning_init_text="Classify if this text is positive or negative:",
+    tokenizer_name_or_path="your-base-model",
+)
+
+# Tạo mô hình đã tinh chỉnh
+model = get_peft_model(model, peft_config)
+```
+
+## Comparison to Other Methods
+
+Khi so sánh với các phương pháp PEFT khác, prompt tuning nổi bật về mặt hiệu quả. Trong khi LoRA cung cấp số lượng tham số và mức sử dụng bộ nhớ thấp nhưng yêu cầu tải bộ điều hợp khi chuyển đổi nhiệm vụ, prompt tuning đạt được mức sử dụng tài nguyên thậm chí còn thấp hơn và cho phép chuyển đổi nhiệm vụ ngay lập tức. Ngược lại, tinh chỉnh toàn bộ đòi hỏi tài nguyên đáng kể và cần các bản sao mô hình riêng biệt cho các nhiệm vụ khác nhau.
+
+| Phương pháp | Tham số | Bộ nhớ | Chuyển đổi nhiệm vụ |
+|--------|------------|---------|----------------|
+| Prompt Tuning | Rất thấp | Tối thiểu | Dễ dàng |
+| LoRA | Thấp | Thấp | Cần tải bộ điều hợp |
+| Tinh chỉnh toàn bộ | Cao | Cao | Cần bản sao mới |
+
+Khi triển khai prompt tuning, hãy bắt đầu với một số lượng nhỏ token ảo (8-16) và chỉ tăng lên nếu độ phức tạp của nhiệm vụ yêu cầu. Khởi tạo bằng văn bản thường mang lại kết quả tốt hơn so với khởi tạo ngẫu nhiên, đặc biệt khi sử dụng văn bản liên quan đến nhiệm vụ. Chiến lược khởi tạo nên phản ánh độ phức tạp của nhiệm vụ mục tiêu của bạn.
+
+Việc huấn luyện đòi hỏi những cân nhắc hơi khác so với tinh chỉnh toàn bộ. Tốc độ huấn luyện cao hơn thường hoạt động tốt, nhưng việc theo dõi cẩn thận các gradient của token prompt là điều cần thiết. Kiểm định thường xuyên trên các ví dụ đa dạng giúp đảm bảo hiệu suất mạnh mẽ trong các tình huống khác nhau.
+
+## Application
+
+Tinh chỉnh prompt vượt trội trong một số tình huống:
+
+1. Triển khai đa tác vụ
+2. Môi trường hạn chế về tài nguyên
+3. Thích ứng nhiệm vụ nhanh chóng
+4. Các ứng dụng nhạy cảm về quyền riêng tư
+
+Khi các mô hình nhỏ hơn, điều chỉnh nhanh trở nên kém cạnh tranh hơn so với tinh chỉnh hoàn toàn. Ví dụ, trên các mô hình như SmolLM2, điều chỉnh prompt ít hiệu quả hơn so với tinh chỉnh đầy đủ. 
+
+## Next Steps
+
+⏭️ Hãy đọc phần [LoRA Adapters Tutorial](./notebooks/finetune_sft_peft.ipynb) để tìm hiểu cách fine-tune mô hình với LoRA adapters
+
+## Resources
+- [PEFT Documentation](https://huggingface.co/docs/peft)
+- [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691)
+- [Hugging Face Cookbook](https://huggingface.co/learn/cookbook/prompt_tuning_peft)

From 076214ea4f90a3ddd5709b46d469075619387415 Mon Sep 17 00:00:00 2001
From: khanhthanhdev <thanhkt27507@gmail.com>
Date: Tue, 24 Dec 2024 15:52:17 +0700
Subject: [PATCH 20/63] translate chapter 3 notebook to vietnamese

---
 .../notebooks/finetune_sft_peft.ipynb         | 189 +++++++++---------
 1 file changed, 94 insertions(+), 95 deletions(-)

diff --git a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
index 0082900b..48d41601 100644
--- a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -40,16 +40,16 @@
    },
    "outputs": [],
    "source": [
-    "# Install the requirements in Google Colab\n",
+    "# Cài đặt các thư viện cần thiết\n",
     "# !pip install transformers datasets trl huggingface_hub\n",
     "\n",
-    "# Authenticate to Hugging Face\n",
+    "# Đăng nhập vào Hugging Face\n",
     "\n",
     "from huggingface_hub import login\n",
     "\n",
     "login()\n",
     "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+    "# Để tiện lợi, hãy tạo một biến môi trường lưu token của bạn như là HF_TOKEN\n"
    ]
   },
   {
@@ -89,10 +89,10 @@
     }
    ],
    "source": [
-    "# Load a sample dataset\n",
+    "# Tải bộ dữ liệu từ Hugging Face\n",
     "from datasets import load_dataset\n",
     "\n",
-    "# TODO: define your dataset and config using the path and name parameters\n",
+    "# TODO: hãy thay đổi path và name cho phù hợp với bộ dữ liệu bạn muốn tải\n",
     "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
     "dataset"
    ]
@@ -103,27 +103,27 @@
     "id": "9TOhJdtsouLk"
    },
    "source": [
-    "## 3. Fine-tune LLM using `trl` and the `SFTTrainer` with LoRA\n",
+    "## 3. Tinh chỉnh LLM sử dụng `trl` và `SFTTrainer` với LoRA\n",
     "\n",
-    "The [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) from `trl` provides integration with LoRA adapters through the [PEFT](https://huggingface.co/docs/peft/en/index) library. Key advantages of this setup include:\n",
+    "[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) từ `trl` cung cấp tích hợp với bộ điều hợp LoRA thông qua thư viện [PEFT](https://huggingface.co/docs/peft/en/index). Những lợi thế chính của cách thiết lập này bao gồm:\n",
     "\n",
-    "1. **Memory Efficiency**: \n",
-    "   - Only adapter parameters are stored in GPU memory\n",
-    "   - Base model weights remain frozen and can be loaded in lower precision\n",
-    "   - Enables fine-tuning of large models on consumer GPUs\n",
+    "1. **Hiệu quả bộ nhớ**: \n",
+    "   - Chỉ các tham số bộ điều hợp được lưu trong bộ nhớ GPU\n",
+    "   - Trọng số mô hình cơ sở vẫn được đóng băng và có thể được tải với độ chính xác thấp hơn\n",
+    "   - Cho phép tinh chỉnh các mô hình lớn trên GPU thông dụng\n",
     "\n",
-    "2. **Training Features**:\n",
-    "   - Native PEFT/LoRA integration with minimal setup\n",
-    "   - Support for QLoRA (Quantized LoRA) for even better memory efficiency\n",
+    "2. **Tính năng huấn luyện**:\n",
+    "   - Tích hợp sẵn PEFT/LoRA với thiết lập tối thiểu\n",
+    "   - Hỗ trợ QLoRA (LoRA lượng tử hóa) để hiệu quả bộ nhớ tốt hơn nữa\n",
     "\n",
-    "3. **Adapter Management**:\n",
-    "   - Adapter weight saving during checkpoints\n",
-    "   - Features to merge adapters back into base model\n",
+    "3. **Quản lý bộ điều hợp**:\n",
+    "   - Lưu trọng số bộ điều hợp trong các điểm kiểm tra\n",
+    "   - Các tính năng để kết hợp bộ điều hợp trở lại mô hình cơ sở\n",
     "\n",
-    "We'll use LoRA in our example, which combines LoRA with 4-bit quantization to further reduce memory usage without sacrificing performance. The setup requires just a few configuration steps:\n",
-    "1. Define the LoRA configuration (rank, alpha, dropout)\n",
-    "2. Create the SFTTrainer with PEFT config\n",
-    "3. Train and save the adapter weights\n"
+    "Chúng ta sẽ sử dụng LoRA trong ví dụ của mình, kết hợp LoRA với lượng tử hóa 4-bit để giảm thêm mức sử dụng bộ nhớ mà không ảnh hưởng đến hiệu suất. Thiết lập chỉ yêu cầu một vài bước cấu hình:\n",
+    "1. Xác định cấu hình LoRA (hạng, alpha, dropout)\n",
+    "2. Tạo SFTTrainer với cấu hình PEFT\n",
+    "3. Huấn luyện và lưu trọng số bộ điều hợp\n"
    ]
   },
   {
@@ -132,7 +132,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Import necessary libraries\n",
+    "# Import các thư viện cần thiết\n",
     "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
     "from datasets import load_dataset\n",
     "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
@@ -144,7 +144,7 @@
     "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
     ")\n",
     "\n",
-    "# Load the model and tokenizer\n",
+    "# Tải mô hình và tokenizer\n",
     "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
     "\n",
     "model = AutoModelForCausalLM.from_pretrained(\n",
@@ -152,10 +152,10 @@
     ").to(device)\n",
     "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
     "\n",
-    "# Set up the chat format\n",
+    "# Tinh chỉnh định dạng \n",
     "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
     "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "# Đặt tên cho mô hình tinh chỉnh\n",
     "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
     "finetune_tags = [\"smol-course\", \"module_1\"]"
    ]
@@ -166,15 +166,15 @@
     "id": "ZbuVArTHouLk"
    },
    "source": [
-    "The `SFTTrainer`  supports a native integration with `peft`, which makes it super easy to efficiently tune LLMs using, e.g. LoRA. We only need to create our `LoraConfig` and provide it to the trainer.\n",
+    "`SFTTrainer` hỗ trợ tích hợp sẵn với `peft`, điều này giúp việc tinh chỉnh hiệu quả các mô hình LLM trở nên cực kỳ dễ dàng, ví dụ như sử dụng LoRA. Chúng ta chỉ cần tạo `LoraConfig` và cung cấp nó cho trình huấn luyện.\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Define LoRA parameters for finetuning</h2>\n",
-    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Use the general parameters for an abitrary finetune</p>\n",
-    "    <p>🐕 Adjust the parameters and review in weights & biases.</p>\n",
-    "    <p>🦁 Adjust the parameters and show change in inference results.</p>\n",
+    "    <h2 style='margin: 0;color:blue'>Bài tập: Xác định tham số LoRA cho tinh chỉnh</h2>\n",
+    "    <p>Lấy một tập dữ liệu từ Hugging Face hub và tinh chỉnh một mô hình trên đó.</p>\n",
+    "    <p><b>Các mức độ khó</b></p>\n",
+    "    <p>🐢 Sử dụng các tham số chung cho một lần tinh chỉnh bất kỳ</p>\n",
+    "    <p>🐕 Điều chỉnh các tham số và đánh giá trong weights & biases</p>\n",
+    "    <p>🦁 Điều chỉnh các tham số và hiển thị sự thay đổi trong kết quả suy luận</p>\n",
     "</div>"
    ]
   },
@@ -188,21 +188,22 @@
    "source": [
     "from peft import LoraConfig\n",
     "\n",
-    "# TODO: Configure LoRA parameters\n",
-    "# r: rank dimension for LoRA update matrices (smaller = more compression)\n",
+    "# TODO: Tinh chỉnh các tham số LoRA \n",
+    "# r: hạng của ma trận LoRA (thường nằm trong khoảng 4-32) , nhỏ hơn tức là sẽ nén nhiều hơn\n",
     "rank_dimension = 6\n",
-    "# lora_alpha: scaling factor for LoRA layers (higher = stronger adaptation)\n",
+    "# lora_alpha: hệ số tỷ lệ cho các lớp LoRA (cao = sự thích nghi mạnh mẽ hơn)\n",
     "lora_alpha = 8\n",
-    "# lora_dropout: dropout probability for LoRA layers (helps prevent overfitting)\n",
+    "# lora_dropout: tỉ lệ dropout cho các lớp LoRA (giúp mô hình tránh tình trạng overfitting)\n",
+    "\n",
     "lora_dropout = 0.05\n",
     "\n",
     "peft_config = LoraConfig(\n",
-    "    r=rank_dimension,  # Rank dimension - typically between 4-32\n",
-    "    lora_alpha=lora_alpha,  # LoRA scaling factor - typically 2x rank\n",
-    "    lora_dropout=lora_dropout,  # Dropout probability for LoRA layers\n",
-    "    bias=\"none\",  # Bias type for LoRA. the corresponding biases will be updated during training.\n",
-    "    target_modules=\"all-linear\",  # Which modules to apply LoRA to\n",
-    "    task_type=\"CAUSAL_LM\",  # Task type for model architecture\n",
+    "    r=rank_dimension,  # hạng của ma trận LoRA\n",
+    "    lora_alpha=lora_alpha,  # hệ số tỷ lệ cho các lớp LoRA \n",
+    "    lora_dropout=lora_dropout,  # tỉ lệ dropout cho các lớp LoRA\n",
+    "    bias=\"none\",  # Loại bias cho LoRA, những bias sẽ được cập nhật trong quá trình huấn luyện\n",
+    "    target_modules=\"all-linear\",  # Những module mà LoRA sẽ được áp dụng\n",
+    "    task_type=\"CAUSAL_LM\",  # Loại bài toán mà mô hình đang giải quyết\n",
     ")"
    ]
   },
@@ -212,7 +213,7 @@
     "id": "l5NUDPcaouLl"
    },
    "source": [
-    "Before we can start our training we need to define the hyperparameters (`TrainingArguments`) we want to use."
+    "Trước khi chúng ta có thể bắt đầu huấn luyện, chúng ta cần xác định các siêu tham số (`TrainingArguments`) mà chúng ta muốn sử dụng."
    ]
   },
   {
@@ -223,33 +224,33 @@
    },
    "outputs": [],
    "source": [
-    "# Training configuration\n",
-    "# Hyperparameters based on QLoRA paper recommendations\n",
+    "# Những cài đặt cho quá trình tinh chỉnh\n",
+    "# Những siêu tham số dựa vào gợi ý từ bài báo QLoRA\n",
     "args = SFTConfig(\n",
-    "    # Output settings\n",
-    "    output_dir=finetune_name,  # Directory to save model checkpoints\n",
-    "    # Training duration\n",
-    "    num_train_epochs=1,  # Number of training epochs\n",
-    "    # Batch size settings\n",
-    "    per_device_train_batch_size=2,  # Batch size per GPU\n",
-    "    gradient_accumulation_steps=2,  # Accumulate gradients for larger effective batch\n",
-    "    # Memory optimization\n",
-    "    gradient_checkpointing=True,  # Trade compute for memory savings\n",
-    "    # Optimizer settings\n",
-    "    optim=\"adamw_torch_fused\",  # Use fused AdamW for efficiency\n",
-    "    learning_rate=2e-4,  # Learning rate (QLoRA paper)\n",
-    "    max_grad_norm=0.3,  # Gradient clipping threshold\n",
-    "    # Learning rate schedule\n",
-    "    warmup_ratio=0.03,  # Portion of steps for warmup\n",
-    "    lr_scheduler_type=\"constant\",  # Keep learning rate constant after warmup\n",
-    "    # Logging and saving\n",
-    "    logging_steps=10,  # Log metrics every N steps\n",
-    "    save_strategy=\"epoch\",  # Save checkpoint every epoch\n",
+    "    # Cài đặt đầu ra\n",
+    "    output_dir=finetune_name,  # Đường dẫn lưu  mô hình tinh chỉnh\n",
+    "    # Thời gian huấn luyện\n",
+    "    num_train_epochs=1,  # Số epoch\n",
+    "    # Cài đặt batch_size\n",
+    "    per_device_train_batch_size=2,  # Batch size trên mỗi GPU\n",
+    "    gradient_accumulation_steps=2,  # Accumulate gradients cho batch_size lớn hơn\n",
+    "    # Tối ưu bộ nhớ\n",
+    "    gradient_checkpointing=True,  # Sử dụng gradient checkpointing để giảm bộ nhớ nhưng tăng thời gian huấn luyện\n",
+    "    # Tinh chỉnh tối ưu hóa\n",
+    "    optim=\"adamw_torch_fused\",  # Sử dụng AdamW\n",
+    "    learning_rate=2e-4,  # Hệ số learning_rate (QLoRA paper)\n",
+    "    max_grad_norm=0.3,  # Giới hạn gradient lớn nhất\n",
+    "    # Cài đặt learning rate\n",
+    "    warmup_ratio=0.03,  # Phần trăm số bước học tăng dần\n",
+    "    lr_scheduler_type=\"constant\",  # Giữ nguyên learning rate\n",
+    "    # Logging và lưu trữ\n",
+    "    logging_steps=10,  # In ra thông tin sau mỗi bước\n",
+    "    save_strategy=\"epoch\",  # Lưu mô hình sau mỗi epoch\n",
     "    # Precision settings\n",
-    "    bf16=True,  # Use bfloat16 precision\n",
-    "    # Integration settings\n",
-    "    push_to_hub=False,  # Don't push to HuggingFace Hub\n",
-    "    report_to=None,  # Disable external logging\n",
+    "    bf16=True,  # Sử dụng bfloat16\n",
+    "    # Những cài đặt khác\n",
+    "    push_to_hub=False,  # Không tải lên HuggingFace Hub\n",
+    "    report_to=None,  # Không gửi kết quả lên các nền tảng khác\n",
     ")"
    ]
   },
@@ -259,7 +260,7 @@
     "id": "cGhR7uFBouLl"
    },
    "source": [
-    "We now have every building block we need to create our `SFTTrainer` to start then training our model."
+    "Bây giờ chúng ta có mọi khối cần thiết để tạo `SFTTrainer` bắt đầu đào tạo mô hình của chúng ta."
    ]
   },
   {
@@ -270,20 +271,20 @@
    },
    "outputs": [],
    "source": [
-    "max_seq_length = 1512  # max sequence length for model and packing of the dataset\n",
+    "max_seq_length = 1512  # độ dài chuỗi tối đa cho mô hình và đóng gói bộ dữ liệu\n",
     "\n",
-    "# Create SFTTrainer with LoRA configuration\n",
+    "# Tạo SFTTrainer với cấu hình LoRA\n",
     "trainer = SFTTrainer(\n",
     "    model=model,\n",
     "    args=args,\n",
     "    train_dataset=dataset[\"train\"],\n",
-    "    peft_config=peft_config,  # LoRA configuration\n",
-    "    max_seq_length=max_seq_length,  # Maximum sequence length\n",
+    "    peft_config=peft_config,  # Cấu hình LoRA\n",
+    "    max_seq_length=max_seq_length,  # Độ dài chuỗi tối đa\n",
     "    tokenizer=tokenizer,\n",
-    "    packing=True,  # Enable input packing for efficiency\n",
+    "    packing=True,  # Bật đóng gói đầu vào để tăng hiệu quả\n",
     "    dataset_kwargs={\n",
-    "        \"add_special_tokens\": False,  # Special tokens handled by template\n",
-    "        \"append_concat_token\": False,  # No additional separator needed\n",
+    "        \"add_special_tokens\": False,  # Các token đặc biệt được xử lý bởi template\n",
+    "        \"append_concat_token\": False,  # Không cần thêm ký tự phân tách\n",
     "    },\n",
     ")"
    ]
@@ -294,7 +295,7 @@
     "id": "zQ_kRN24ouLl"
    },
    "source": [
-    "Start training our model by calling the `train()` method on our `Trainer` instance. This will start the training loop and train our model for 3 epochs. Since we are using a PEFT method, we will only save the adapted model weights and not the full model."
+    "Bắt đầu huấn luyện mô hình của chúng ta bằng cách gọi phương thức `train()` trên đối tượng `Trainer`. Điều này sẽ bắt đầu vòng lặp huấn luyện và huấn luyện mô hình của chúng ta trong 3 epoch. Vì chúng ta đang sử dụng phương pháp PEFT, chúng ta sẽ chỉ lưu các trọng số mô hình đã được điều chỉnh và không lưu toàn bộ mô hình."
    ]
   },
   {
@@ -330,10 +331,10 @@
     }
    ],
    "source": [
-    "# start training, the model will be automatically saved to the hub and the output directory\n",
+    "# bắt đầu huấn luyện\n",
     "trainer.train()\n",
     "\n",
-    "# save model\n",
+    "# lưu mô hình\n",
     "trainer.save_model()"
    ]
   },
@@ -343,8 +344,7 @@
     "id": "y4HHSYYzouLl"
    },
    "source": [
-    "The training with Flash Attention for 3 epochs with a dataset of 15k samples took 4:14:36 on a `g5.2xlarge`. The instance costs `1.21$/h` which brings us to a total cost of only ~`5.3$`.\n",
-    "\n"
+    "Việc huấn luyện với Flash Attention trong 3 epoch với tập dữ liệu gồm 15k mẫu mất 4:14:36 trên `g5.2xlarge`. Chi phí của phiên bản này là `1.21$/h`, tổng chi phí chỉ khoảng ~`5.3$`.\n"
    ]
   },
   {
@@ -353,13 +353,13 @@
     "id": "C309KsXjouLl"
    },
    "source": [
-    "### Merge LoRA Adapter into the Original Model\n",
+    "### Hợp nhất adapter LoRA vào mô hình gốc\n",
     "\n",
-    "When using LoRA, we only train adapter weights while keeping the base model frozen. During training, we save only these lightweight adapter weights (~2-10MB) rather than a full model copy. However, for deployment, you might want to merge the adapters back into the base model for:\n",
+    "Khi sử dụng LoRA, chúng ta chỉ huấn luyện các trọng số của adapter trong khi giữ nguyên mô hình gốc. Trong quá trình huấn luyện, chúng ta chỉ lưu các trọng số nhẹ của adapter (~2-10MB) thay vì một bản sao đầy đủ của mô hình. Tuy nhiên, để triển khai, bạn có thể muốn hợp nhất các adapter trở lại mô hình gốc vì:\n",
     "\n",
-    "1. **Simplified Deployment**: Single model file instead of base model + adapters\n",
-    "2. **Inference Speed**: No adapter computation overhead\n",
-    "3. **Framework Compatibility**: Better compatibility with serving frameworks\n"
+    "1. **Triển khai đơn giản**: Một tệp mô hình thay vì mô hình gốc + bộ adapter\n",
+    "2. **Tốc độ suy luận**: Không cần tính toán adapter\n",
+    "3. **Tương thích với framework**: Tương thích tốt hơn với các framework\n"
    ]
   },
   {
@@ -371,14 +371,14 @@
     "from peft import AutoPeftModelForCausalLM\n",
     "\n",
     "\n",
-    "# Load PEFT model on CPU\n",
+    "# Tải PEFT vào CPU\n",
     "model = AutoPeftModelForCausalLM.from_pretrained(\n",
     "    pretrained_model_name_or_path=args.output_dir,\n",
     "    torch_dtype=torch.float16,\n",
     "    low_cpu_mem_usage=True,\n",
     ")\n",
     "\n",
-    "# Merge LoRA and base model and save\n",
+    "# Kết hợp các trọng số trong Adapter và LoRA sau đó lưu mô hình\n",
     "merged_model = model.merge_and_unload()\n",
     "merged_model.save_pretrained(\n",
     "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
@@ -391,10 +391,9 @@
     "id": "-yO6E9quouLl"
    },
    "source": [
-    "## 3. Test Model and run Inference\n",
+    "## 3. Kiểm thử mô hình và chạy thử\n",
     "\n",
-    "After the training is done we want to test our model. We will load different samples from the original dataset and evaluate the model on those samples, using a simple loop and accuracy as our metric.\n",
-    "\n"
+    "Sau khi huấn luyện xong, chúng ta muốn kiểm thử mô hình của mình. Chúng ta sẽ tải các mẫu khác nhau từ bộ dữ liệu gốc và đánh giá mô hình trên các mẫu đó, sử dụng một vòng lặp đơn giản và độ chính xác làm thước đo.\n"
    ]
   },
   {
@@ -402,8 +401,8 @@
    "metadata": {},
    "source": [
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Bonus Exercise: Load LoRA Adapter</h2>\n",
-    "    <p>Use what you learnt from the ecample note book to load your trained LoRA adapter for inference.</p> \n",
+    "    <h2 style='margin: 0;color:blue'>Bài tập bổ sung: tải LoRA Adapter</h2>\n",
+    "    <p>Sử dụng những gì bạn học được từ ghi chú để tải LoRA Adapter đã được đào tạo của bạn để chạy</p> \n",
     "</div>"
    ]
   },
@@ -415,7 +414,7 @@
    },
    "outputs": [],
    "source": [
-    "# free the memory again\n",
+    "# dọn dẹp bộ nhớ\n",
     "del model\n",
     "del trainer\n",
     "torch.cuda.empty_cache()"
@@ -449,7 +448,7 @@
     "id": "99uFDAuuouLl"
    },
    "source": [
-    "Lets test some prompt samples and see how the model performs."
+    "Hãy thử một số ví dụ để xem mô hình hoạt động"
    ]
   },
   {

From 909371fdd5800994fa0caac2bba5fa30c641d7cf Mon Sep 17 00:00:00 2001
From: Sifal <sifalklioui@yahoo.com>
Date: Tue, 24 Dec 2024 18:01:28 +0100
Subject: [PATCH 21/63] updating weights / training model

I understood, but I prefer the usual formulation
---
 1_instruction_tuning/supervised_fine_tuning.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1_instruction_tuning/supervised_fine_tuning.md b/1_instruction_tuning/supervised_fine_tuning.md
index cdd10607..dc236962 100644
--- a/1_instruction_tuning/supervised_fine_tuning.md
+++ b/1_instruction_tuning/supervised_fine_tuning.md
@@ -16,7 +16,7 @@ For example, if you're developing a customer service application, you might want
 
 ## The Fine-Tuning Process
 
-The supervised fine-tuning process involves training model weights on a task-specific dataset. 
+The supervised fine-tuning process involves adjusting a model's weights on a task-specific dataset. 
 
 First, you'll need to prepare or select a dataset that represents your target task. This dataset should include diverse examples that cover the range of scenarios your model will encounter. The quality of this data is important - each example should demonstrate the kind of output you want your model to produce. Next comes the actual fine-tuning phase, where you'll use frameworks like Hugging Face's `transformers` and `trl` to train the model on your dataset. 
 

From 0b7675edbec54f81b452fc5bd30fef23a9623ae8 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Mon, 30 Dec 2024 07:27:29 +0100
Subject: [PATCH 22/63] first commit

---
 7_inference/README.md                    |  36 +++++
 7_inference/inference_pipeline.md        | 174 +++++++++++++++++++++++
 7_inference/text_generation_inference.md | 137 ++++++++++++++++++
 3 files changed, 347 insertions(+)
 create mode 100644 7_inference/inference_pipeline.md
 create mode 100644 7_inference/text_generation_inference.md

diff --git a/7_inference/README.md b/7_inference/README.md
index e69de29b..f609f57f 100644
--- a/7_inference/README.md
+++ b/7_inference/README.md
@@ -0,0 +1,36 @@
+# Inference
+
+Inference is the process of using a trained language model to generate predictions or responses. While inference might seem straightforward, deploying models efficiently at scale requires careful consideration of various factors like performance, cost, and reliability. Large Language Models (LLMs) present unique challenges due to their size and computational requirements.
+
+We'll explore both simple and production-ready approaches using the [`transformers`](https://huggingface.co/docs/transformers/index) library and [`text-generation-inference`](https://github.com/huggingface/text-generation-inference), two popular frameworks for LLM inference. For production deployments, we'll focus on Text Generation Inference (TGI), which provides optimized serving capabilities.
+
+## Module Overview
+
+LLM inference can be categorized into two main approaches: simple pipeline-based inference for development and testing, and optimized serving solutions for production deployments. We'll cover both approaches, starting with the simpler pipeline approach and moving to production-ready solutions.
+
+## Contents
+
+### 1. [Basic Pipeline Inference](./pipeline_inference.md)
+
+Learn how to use the Hugging Face Transformers pipeline for basic inference. We'll cover setting up pipelines, configuring generation parameters, and best practices for local development. The pipeline approach is perfect for prototyping and small-scale applications. [Start learning](./pipeline_inference.md).
+
+### 2. [Production Inference with TGI](./tgi_inference.md)
+
+Learn how to deploy models for production using Text Generation Inference. We'll explore optimized serving techniques, batching strategies, and monitoring solutions. TGI provides production-ready features like health checks, metrics, and Docker deployment options. [Start learning](./tgi_inference.md).
+
+### Exercise Notebooks
+
+| Title | Description | Exercise | Link | Colab |
+|-------|-------------|----------|------|-------|
+| Pipeline Inference | Basic inference with transformers pipeline | 🐢 Set up a basic pipeline <br> 🐕 Configure generation parameters <br> 🦁 Create a simple web server | [Link](./notebooks/basic_pipeline_inference.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/basic_pipeline_inference.ipynb) |
+| TGI Deployment | Production deployment with TGI | 🐢 Deploy a model with TGI <br> 🐕 Configure performance optimizations <br> 🦁 Set up monitoring and scaling | [Link](./notebooks/tgi_deployment.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/7_inference/notebooks/tgi_deployment.ipynb) |
+
+## Resources
+
+- [Hugging Face Pipeline Tutorial](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
+- [Text Generation Inference Documentation](https://huggingface.co/docs/text-generation-inference/en/index)
+- [Pipeline WebServer Guide](https://huggingface.co/docs/transformers/en/pipeline_tutorial#using-pipelines-for-a-webserver)
+- [TGI GitHub Repository](https://github.com/huggingface/text-generation-inference)
+- [Hugging Face Model Deployment Documentation](https://huggingface.co/docs/inference-endpoints/index)
+- [vLLM: High-throughput LLM Serving](https://github.com/vllm-project/vllm)
+- [Optimizing Transformer Inference](https://huggingface.co/blog/optimize-transformer-inference)
\ No newline at end of file
diff --git a/7_inference/inference_pipeline.md b/7_inference/inference_pipeline.md
new file mode 100644
index 00000000..cff809bf
--- /dev/null
+++ b/7_inference/inference_pipeline.md
@@ -0,0 +1,174 @@
+# Basic Inference with Transformers Pipeline
+
+The `pipeline` abstraction in 🤗 Transformers provides a simple way to run inference with any model from the Hugging Face Hub. It handles all the preprocessing and postprocessing steps, making it easy to use models without deep knowledge of their architecture or requirements.
+
+## How Pipelines Work
+
+Hugging Face pipelines streamline the machine learning workflow by automating three critical stages between raw input and human-readable output:
+
+**Preprocessing Stage**
+The pipeline first prepares your raw inputs for the model. This varies by input type:
+- Text inputs undergo tokenization to convert words into model-friendly token IDs
+- Images are resized and normalized to match model requirements
+- Audio is processed through feature extraction to create spectrograms or other representations
+
+**Model Inference**
+During the forward pass, the pipeline:
+- Handles batching of inputs automatically for efficient processing
+- Places computation on the optimal device (CPU/GPU)
+- Applies performance optimizations like half-precision (FP16) inference where supported
+
+**Postprocessing Stage**
+Finally, the pipeline converts raw model outputs into useful results:
+- Decodes token IDs back into readable text
+- Transforms logits into probability scores
+- Formats outputs according to the specific task (e.g., classification labels, generated text)
+
+This abstraction lets you focus on your application logic while the pipeline handles the technical complexity of model inference.
+
+## Basic Usage
+
+Here's how to use a pipeline for text generation:
+
+```python
+from transformers import pipeline
+
+# Create a pipeline with a specific model
+generator = pipeline(
+    "text-generation",
+    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Generate text
+response = generator(
+    "Write a short poem about coding:",
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7
+)
+print(response[0]['generated_text'])
+```
+
+## Key Configuration Options
+
+### Model Loading
+```python
+# CPU inference
+generator = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", device="cpu")
+
+# GPU inference (device 0)
+generator = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", device=0)
+
+# Automatic device placement
+generator = pipeline(
+    "text-generation",
+    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    device_map="auto",
+    torch_dtype="auto"
+)
+```
+
+### Generation Parameters
+```python
+response = generator(
+    "Translate this to French:",
+    max_new_tokens=100,     # Maximum length of generated text
+    do_sample=True,         # Use sampling instead of greedy decoding
+    temperature=0.7,        # Control randomness (higher = more random)
+    top_k=50,               # Limit to top k tokens
+    top_p=0.95,             # Nucleus sampling threshold
+    num_return_sequences=1  # Number of different generations
+)
+```
+
+## Processing Multiple Inputs
+
+Pipelines can efficiently handle multiple inputs through batching:
+
+```python
+# Prepare multiple prompts
+prompts = [
+    "Write a haiku about programming:",
+    "Explain what an API is:",
+    "Write a short story about a robot:"
+]
+
+# Process all prompts efficiently
+responses = generator(
+    prompts,
+    batch_size=4,              # Number of prompts to process together
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7
+)
+
+# Print results
+for prompt, response in zip(prompts, responses):
+    print(f"Prompt: {prompt}")
+    print(f"Response: {response[0]['generated_text']}\n")
+```
+
+## Web Server Integration
+
+Here's how to integrate a pipeline into a Flask application:
+
+```python
+from flask import Flask, request, jsonify
+from transformers import pipeline
+
+app = Flask(__name__)
+
+# Initialize pipeline globally
+generator = pipeline(
+    "text-generation",
+    model="HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    device_map="auto"
+)
+
+@app.route("/generate", methods=["POST"])
+def generate_text():
+    try:
+        data = request.json
+        prompt = data.get("prompt", "")
+        
+        # Input validation
+        if not prompt:
+            return jsonify({"error": "No prompt provided"}), 400
+            
+        # Generate response with timeout
+        response = generator(
+            prompt,
+            max_new_tokens=100,
+            do_sample=True,
+            temperature=0.7
+        )
+        
+        return jsonify({
+            "generated_text": response[0]['generated_text']
+        })
+        
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)
+```
+
+## Limitations
+
+While pipelines are great for prototyping and small-scale deployments, they have some limitations:
+
+- Limited optimization options compared to dedicated serving solutions
+- No built-in support for advanced features like dynamic batching
+- May not be suitable for high-throughput production workloads
+
+For production deployments with high throughput requirements, consider using Text Generation Inference (TGI) or other specialized serving solutions.
+
+## Resources
+
+- [Hugging Face Pipeline Tutorial](https://huggingface.co/docs/transformers/en/pipeline_tutorial)
+- [Pipeline API Reference](https://huggingface.co/docs/transformers/en/main_classes/pipelines)
+- [Text Generation Parameters](https://huggingface.co/docs/transformers/en/main_classes/text_generation)
+- [Model Quantization Guide](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one)
\ No newline at end of file
diff --git a/7_inference/text_generation_inference.md b/7_inference/text_generation_inference.md
new file mode 100644
index 00000000..1552c366
--- /dev/null
+++ b/7_inference/text_generation_inference.md
@@ -0,0 +1,137 @@
+# Text Generation Inference (TGI)
+
+Text Generation Inference (TGI) is a toolkit developed by Hugging Face for deploying and serving Large Language Models (LLMs). It's designed to enable high-performance text generation for popular open-source LLMs. TGI is used in production by Hugging Chat - An open-source interface for open-access models.
+
+## Why Use Text Generation Inference?
+
+Text Generation Inference addresses the key challenges of deploying large language models in production. While many frameworks excel at model development, TGI specifically optimizes for production deployment and scaling. Some key features include:
+
+- **Tensor Parallelism**: TGI's can split models across multiple GPUs through tensor parallelism, essential for serving larger models efficiently.
+- **Continuous Batching**: The continuous batching system maximizes GPU utilization by dynamically processing requests, while optimizations like Flash Attention and Paged Attention significantly reduce memory usage and increase speed.
+- **Token Streaming**: Real-time applications benefit from token streaming via Server-Sent Events, delivering responses with minimal latency.
+
+
+## How to Use Text Generation Inference
+
+### Basic Python Usage
+
+TGI uses a simple yet powerful REST API integration which makes it easy to integrate with your applications. 
+
+### Using the REST API
+
+TGI exposes a RESTful API that accepts JSON payloads. This makes it accessible from any programming language or tool that can make HTTP requests. Here's a basic example using curl:
+
+```bash
+# Basic generation request
+curl localhost:8080/v1/chat/completions \
+    -X POST \
+    -d '{
+  "model": "tgi",
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant."
+    },
+    {
+      "role": "user",
+      "content": "What is deep learning?"
+    }
+  ],
+  "stream": true,
+  "max_tokens": 20
+}' \
+    -H 'Content-Type: application/json'
+```
+
+### Using the `huggingface_hub` Python Client
+
+The `huggingface_hub` python client client handles connection management, request formatting, and response parsing. Here's how to get started.
+
+```python
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(
+    base_url="http://localhost:8080/v1/",
+)
+
+output = client.chat.completions.create(
+    model="tgi",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Count to 10"},
+    ],
+    stream=True,
+    max_tokens=1024,
+)
+
+for chunk in output:
+    print(chunk.choices[0].delta.content)
+```
+
+
+### Using OpenAI API
+
+Many libraries support the OpenAI API, so you can use the same client to interact with TGI.
+
+```python
+from openai import OpenAI
+
+# init the client but point it to TGI
+client = OpenAI(
+    base_url="http://localhost:8080/v1/",
+    api_key="-"
+)
+
+chat_completion = client.chat.completions.create(
+    model="tgi",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant." },
+        {"role": "user", "content": "What is deep learning?"}
+    ],
+    stream=True
+)
+
+# iterate and print stream
+for message in chat_completion:
+    print(message)
+```
+
+## Preparing Models for TGI
+
+To serve a model with TGI, ensure it meets these requirements:
+
+1. **Supported Architecture**: Verify your model architecture is supported (Llama, BLOOM, T5, etc.)
+
+2. **Model Format**: Convert weights to safetensors format for faster loading:
+
+```python
+from safetensors.torch import save_file
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("your-model")
+state_dict = model.state_dict()
+save_file(state_dict, "model.safetensors")
+```
+
+3. **Quantization** (optional): Quantize your model to reduce memory usage:
+
+```python
+from transformers import BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype="float16"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "your-model",
+    quantization_config=quantization_config
+)
+```
+
+## References
+
+- [Text Generation Inference Documentation](https://huggingface.co/docs/text-generation-inference)
+- [TGI GitHub Repository](https://github.com/huggingface/text-generation-inference)
+- [Hugging Face Model Hub](https://huggingface.co/models)
+- [TGI API Reference](https://huggingface.co/docs/text-generation-inference/api_reference)
\ No newline at end of file

From ea273607ed4bc405fd2ae0294230707e23db2cb6 Mon Sep 17 00:00:00 2001
From: Alex Strick van Linschoten <stricksubscriptions@fastmail.fm>
Date: Mon, 30 Dec 2024 16:54:03 +0100
Subject: [PATCH 23/63] Add gitleaks to check for secrets

---
 .github/workflows/gitleaks.yml | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 .github/workflows/gitleaks.yml

diff --git a/.github/workflows/gitleaks.yml b/.github/workflows/gitleaks.yml
new file mode 100644
index 00000000..6afb2b01
--- /dev/null
+++ b/.github/workflows/gitleaks.yml
@@ -0,0 +1,16 @@
+name: gitleaks
+on:
+  pull_request:
+  push:
+  workflow_dispatch:
+jobs:
+  scan:
+    name: gitleaks
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - uses: gitleaks/gitleaks-action@v2
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

From 30a81a08baa3160dd072e59da32c56f2cf538ef3 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 1 Jan 2025 23:10:59 +0100
Subject: [PATCH 24/63] init

---
 vi/4_evaluation/custom_evaluation.md | 97 ++++++++++++++--------------
 1 file changed, 49 insertions(+), 48 deletions(-)

diff --git a/vi/4_evaluation/custom_evaluation.md b/vi/4_evaluation/custom_evaluation.md
index c82868a7..3461cb48 100644
--- a/vi/4_evaluation/custom_evaluation.md
+++ b/vi/4_evaluation/custom_evaluation.md
@@ -1,16 +1,16 @@
-# Custom Domain Evaluation
+# Đánh Giá Theo Lĩnh Vực Cụ Thể
 
-While standard benchmarks provide valuable insights, many applications require specialized evaluation approaches tailored to specific domains or use cases. This guide will help you create custom evaluation pipelines that accurately assess your model's performance in your target domain.
+Mặc dù các phương pháp đánh giá tiêu chuẩn (`benchmark`) cung cấp những thông tin chuyên sâu có giá trị, nhiều ứng dụng đòi hỏi các phương pháp đánh giá chuyên biệt phù hợp với các lĩnh vực hoặc trường hợp sử dụng cụ thể. Bài học này sẽ giúp bạn tạo các quy trình (`pipeline`) tùy chỉnh các phương pháp đánh giá để có cái nhìn chính xác hơn về hiệu suất của mô hình trong lĩnh vực cụ thể của bạn.
 
-## Designing Your Evaluation Strategy
+## Thiết kế chiến lược đánh giá của bạn
 
-A successful custom evaluation strategy starts with clear objectives. Consider what specific capabilities your model needs to demonstrate in your domain. This might include technical knowledge, reasoning patterns, or domain-specific formats. Document these requirements carefully - they'll guide both your task design and metric selection.
+Một chiến lược đánh giá tùy chỉnh đầy đủ bắt đầu với các mục tiêu rõ ràng. Hãy xem xét những khả năng cụ thể mà mô hình của bạn cần thể hiện trong lĩnh vực của bạn. Điều này có thể bao gồm kiến thức kỹ thuật, các mẫu suy luận hoặc các định dạng đặc thù cho lĩnh vực đó. Ghi lại cẩn thận các yêu cầu này - chúng sẽ hướng dẫn cả việc thiết kế tác vụ (`task`) và lựa chọn các chỉ số (`metric`).
 
-Your evaluation should test both standard use cases and edge cases. For example, in a medical domain, you might evaluate both common diagnostic scenarios and rare conditions. In financial applications, you might test both routine transactions and complex edge cases involving multiple currencies or special conditions.
+Việc đánh giá của bạn nên kiểm tra cả các trường hợp sử dụng tiêu chuẩn và các trường hợp biên (`edge case`). Ví dụ, trong lĩnh vực y tế, bạn có thể đánh giá cả các kịch bản chẩn đoán phổ biến và các tình trạng hiếm gặp. Trong các ứng dụng tài chính, bạn có thể kiểm tra cả các giao dịch thông thường và các trường hợp biên phức tạp liên quan đến nhiều loại tiền tệ hoặc các điều kiện đặc biệt.
 
-## Implementation with LightEval
+## Triển khai với LightEval
 
-LightEval provides a flexible framework for implementing custom evaluations. Here's how to create a custom task:
+`LightEval` cung cấp một khung (`framework`) linh hoạt để triển khai các đánh giá tùy chỉnh. Đây là cách để tạo một tác vụ (`task`) tùy chỉnh:
 
 ```python
 from lighteval.tasks import Task, Doc
@@ -21,62 +21,63 @@ class CustomEvalTask(Task):
         super().__init__(
             name="custom_task",
             version="0.0.1",
-            metrics=["accuracy", "f1"],  # Your chosen metrics
-            description="Description of your custom evaluation task"
+            metrics=["accuracy", "f1"],  # Các chỉ số bạn chọn
+            description="Mô tả tác vụ đánh giá tùy chỉnh của bạn"
         )
     
     def get_prompt(self, sample):
-        # Format your input into a prompt
+        # Định dạng đầu vào của bạn thành một lời nhắc (prompt)
         return f"Question: {sample['question']}\nAnswer:"
     
     def process_response(self, response, ref):
-        # Process model output and compare to reference
+        # Xử lý đầu ra của mô hình và so sánh với tham chiếu
         return response.strip() == ref.strip()
 ```
 
-## Custom Metrics
 
-Domain-specific tasks often require specialized metrics. LightEval provides a flexible framework for creating custom metrics that capture domain-relevant aspects of performance:
+## Các chỉ số (`metric`) tùy chỉnh
+
+Các tác vụ theo lĩnh vực cụ thể thường yêu cầu các chỉ số chuyên biệt. `LightEval` cung cấp một khung linh hoạt để tạo các chỉ số tùy chỉnh nắm bắt các khía cạnh liên quan đến lĩnh vực của hiệu suất:
 
 ```python
 from aenum import extend_enum
 from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
 import numpy as np
 
-# Define a sample-level metric function
+# Định nghĩa một hàm chỉ số mức mẫu (sample-level metric)
 def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
-    """Example metric that returns multiple scores per sample"""
+    """Chỉ số ví dụ trả về nhiều điểm số cho mỗi mẫu"""
     response = predictions[0]
     return {
         "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
         "length_match": len(response) == len(formatted_doc.reference)
     }
 
-# Create a metric that returns multiple values per sample
+# Tạo một chỉ số trả về nhiều giá trị cho mỗi mẫu
 custom_metric_group = SampleLevelMetricGrouping(
-    metric_name=["accuracy", "length_match"],  # Names of sub-metrics
-    higher_is_better={  # Whether higher values are better for each metric
+    metric_name=["accuracy", "length_match"],  # Tên của các chỉ số
+    higher_is_better={  # Liệu giá trị cao hơn có tốt hơn cho mỗi chỉ số không
         "accuracy": True,
         "length_match": True
     },
     category=MetricCategory.CUSTOM,
     use_case=MetricUseCase.SCORING,
     sample_level_fn=custom_metric,
-    corpus_level_fn={  # How to aggregate each metric
+    corpus_level_fn={  # Cách tổng hợp từng chỉ số
         "accuracy": np.mean,
         "length_match": np.mean
     }
 )
 
-# Register the metric with LightEval
+# Đăng ký chỉ số vào LightEval
 extend_enum(Metrics, "custom_metric_name", custom_metric_group)
 ```
 
-For simpler cases where you only need one metric value per sample:
+Đối với các trường hợp đơn giản hơn, nơi bạn chỉ cần một giá trị chỉ số cho mỗi mẫu:
 
 ```python
 def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
-    """Example metric that returns a single score per sample"""
+    """Chỉ số ví dụ trả về một điểm duy nhất cho mỗi mẫu"""
     response = predictions[0]
     return response == formatted_doc.choices[formatted_doc.gold_index]
 
@@ -86,47 +87,47 @@ simple_metric_obj = SampleLevelMetric(
     category=MetricCategory.CUSTOM,
     use_case=MetricUseCase.SCORING,
     sample_level_fn=simple_metric,
-    corpus_level_fn=np.mean  # How to aggregate across samples
+    corpus_level_fn=np.mean  # Cách tổng hợp trên các mẫu
 )
 
 extend_enum(Metrics, "simple_metric", simple_metric_obj)
 ```
 
-You can then use your custom metrics in your evaluation tasks by referencing them in the task configuration. The metrics will be automatically computed across all samples and aggregated according to your specified functions.
+Sau đó, bạn có thể sử dụng các chỉ số tùy chỉnh của mình trong các tác vụ đánh giá bằng cách tham chiếu chúng trong cấu hình tác vụ. Các chỉ số sẽ được tự động tính toán trên tất cả các mẫu và tổng hợp theo các hàm bạn đã chỉ định.
 
-For more complex metrics, consider:
-- Using metadata in your formatted documents to weight or adjust scores
-- Implementing custom aggregation functions for corpus-level statistics
-- Adding validation checks for your metric inputs
-- Documenting edge cases and expected behavior
+Đối với các chỉ số phức tạp hơn, hãy xem xét:
+- Sử dụng siêu dữ liệu (`metadata`) trong các tài liệu được định dạng của bạn để đánh trọng số hoặc điều chỉnh điểm số
+- Triển khai các hàm tổng hợp tùy chỉnh cho các thống kê cấp độ kho ngữ liệu (`corpus-level`)
+- Thêm kiểm tra xác thực cho đầu vào chỉ số của bạn
+- Ghi lại các trường hợp biên và hành vi mong đợi
 
-For a complete example of custom metrics in action, see our [domain evaluation project](./project/README.md).
+Để có một ví dụ đầy đủ về các chỉ số tùy chỉnh trong thực tế, hãy xem [dự án đánh giá theo lĩnh vực](./project/README.md) của chúng tôi.
 
-## Dataset Creation
+## Tạo tập dữ liệu
 
-High-quality evaluation requires carefully curated datasets. Consider these approaches for dataset creation:
+Đánh giá chất lượng cao đòi hỏi các tập dữ liệu được sắp xếp cẩn thận. Hãy xem xét các phương pháp sau để tạo tập dữ liệu:
 
-1. Expert Annotation: Work with domain experts to create and validate evaluation examples. Tools like [Argilla](https://github.com/argilla-io/argilla) make this process more efficient.
+1. Chú thích của chuyên gia: Làm việc với các chuyên gia trong lĩnh vực để tạo và xác thực các ví dụ đánh giá. Các công cụ như [Argilla](https://github.com/argilla-io/argilla) làm cho quá trình này hiệu quả hơn.
 
-2. Real-World Data: Collect and anonymize real usage data, ensuring it represents actual deployment scenarios.
+2. Dữ liệu thế giới thực: Thu thập và ẩn danh hóa dữ liệu sử dụng thực tế, đảm bảo nó đại diện cho các kịch bản triển khai thực tế.
 
-3. Synthetic Generation: Use LLMs to generate initial examples, then have experts validate and refine them.
+3. Tạo tổng hợp: Sử dụng `LLM` để tạo các ví dụ ban đầu, sau đó để các chuyên gia xác thực và tinh chỉnh chúng.
 
-## Best Practices
+## Các phương pháp tốt nhất
 
-- Document your evaluation methodology thoroughly, including any assumptions or limitations
-- Include diverse test cases that cover different aspects of your domain
-- Consider both automated metrics and human evaluation where appropriate
-- Version control your evaluation datasets and code
-- Regularly update your evaluation suite as you discover new edge cases or requirements
+- Ghi lại phương pháp đánh giá của bạn một cách kỹ lưỡng, bao gồm mọi giả định hoặc hạn chế
+- Bao gồm các trường hợp kiểm thử đa dạng bao gồm các khía cạnh khác nhau trong lĩnh vực của bạn
+- Xem xét cả các chỉ số tự động và đánh giá thủ công khi thích hợp
+- Kiểm soát phiên bản (`Version control`) các tập dữ liệu và mã đánh giá của bạn
+- Thường xuyên cập nhật bộ đánh giá của bạn khi bạn phát hiện ra các trường hợp biên hoặc yêu cầu mới
 
-## References
+## Tài liệu tham khảo
 
-- [LightEval Custom Task Guide](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [LightEval Custom Metrics](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Argilla Documentation](https://docs.argilla.io) for dataset annotation
-- [Evaluation Guidebook](https://github.com/huggingface/evaluation-guidebook) for general evaluation principles
+- [Hướng dẫn tác vụ tùy chỉnh LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Chỉ số tùy chỉnh LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Tài liệu Argilla](https://docs.argilla.io) để chú thích tập dữ liệu
+- [Hướng dẫn đánh giá](https://github.com/huggingface/evaluation-guidebook) cho các nguyên tắc đánh giá chung
 
-# Next Steps
+# Các bước tiếp theo
 
-⏩ For a complete example of implementing these concepts, see our [domain evaluation project](./project/README.md).
\ No newline at end of file
+⏩ Để có một ví dụ đầy đủ về việc triển khai các khái niệm này, hãy xem [dự án đánh giá theo lĩnh vực](./project/README.md) của chúng tôi.
\ No newline at end of file

From ca3eaf0b58d218420d17b708059a17636e0f7c52 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 1 Jan 2025 23:38:12 +0100
Subject: [PATCH 25/63] init

---
 ...hteval_evaluate_and_analyse_your_LLM.ipynb | 66 +++++++++----------
 vi/4_evaluation/project/README.md             | 66 +++++++++----------
 2 files changed, 65 insertions(+), 67 deletions(-)

diff --git a/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
index 9597634b..92d67ae1 100644
--- a/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
+++ b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
@@ -6,24 +6,24 @@
         "id": "TZVw9f5QYWPL"
       },
       "source": [
-        "# lighteval is your AI evaluation library\n",
+        "# `lighteval` là thư viện đánh giá AI của bạn\n",
         "\n",
-        "This notebook explores how you can use lighteval to evaluate and compare LLMs.\n",
+        "Notebook này khám phá cách bạn có thể sử dụng `lighteval` để đánh giá và so sánh các `LLM`.\n",
         "\n",
-        "`lighteval` has been around a while and it's a great tool for getting eval score on major benchmarks. It's just been refactored to support being used like a library in Python, which makes it great for comparing models across benchmarks.\n",
+        "`lighteval` đã xuất hiện được một thời gian và nó là một công cụ tuyệt vời để đạt được điểm đánh giá trên các `benchmark` chính. Nó vừa được tái cấu trúc để hỗ trợ việc sử dụng như một thư viện trong Python, điều này làm cho nó trở nên tuyệt vời để so sánh các mô hình trên các `benchmark`.\n",
         "\n",
-        "So let's dig in to some eval scores.\n",
+        "Vì vậy, hãy đi sâu vào một số điểm đánh giá.\n",
         "\n",
         "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-        "    <h2 style='margin: 0;color:blue'>Exercise: Evaluate Your Own Model</h2>\n",
-        "    <p>Now that you've seen how to evaluate models on specific domains, try evaluating a model on a domain that interests you.</p>\n",
-        "    <p><b>Difficulty Levels</b></p>\n",
-        "    <p>🐢 Use the existing medical domain tasks but evaluate a different model from the Hugging Face hub</p>\n",
-        "    <p>🐕 Create a new domain evaluation by selecting different MMLU tasks (e.g., computer science, mathematics, physics)</p>\n",
-        "    <p>🦁 Create a custom evaluation task using LightEval's task framework and evaluate models on your specific domain</p>\n",
+        "    <h2 style='margin: 0;color:blue'>Bài tập: Đánh giá mô hình của riêng bạn</h2>\n",
+        "    <p>Bây giờ bạn đã thấy cách đánh giá các mô hình trên các lĩnh vực cụ thể, hãy thử đánh giá một mô hình trên một lĩnh vực mà bạn quan tâm.</p>\n",
+        "    <p><b>Các mức độ khó</b></p>\n",
+        "    <p>🐢 Sử dụng các tác vụ lĩnh vực y tế hiện có nhưng đánh giá một mô hình khác từ Hugging Face Hub</p>\n",
+        "    <p>🐕 Tạo một đánh giá lĩnh vực mới bằng cách chọn các tác vụ MMLU khác nhau (ví dụ: khoa học máy tính, toán học, vật lý)</p>\n",
+        "    <p>🦁 Tạo một tác vụ đánh giá tùy chỉnh bằng cách sử dụng khung tác vụ của LightEval và đánh giá các mô hình trên lĩnh vực cụ thể của bạn</p>\n",
         "</div>\n",
         "\n",
-        "## Install dependencies"
+        "## Cài đặt các thư viện"
       ]
     },
     {
@@ -63,17 +63,17 @@
         "id": "TDKs5ShvXw8K"
       },
       "source": [
-        "## Setup `lighteval` evaluation\n",
+        "## Thiết lập đánh giá `lighteval`\n",
         "\n",
-        "We need to setup the evaluation environment and pipeline. Much of this we will disable because we're keeping things in the notebook, but we could also use `push_to_hub` or `push_to_tensorboard`.\n",
+        "Chúng ta cần thiết lập môi trường và quy trình (`pipeline`) đánh giá. Bài tập này sẽ lưu mọi thứ trong notebook, nhưng bạn cũng có thể sử dụng `push_to_hub` hoặc `push_to_tensorboard` để lưu trữ các tập đánh giá để sử dụng về sau.\n",
         "\n",
         "### `push_to_hub`\n",
         "\n",
-        "This is useful if we're evaluating a model and want to persist its evaluation with weights and configuration on the Hugging Face hub.\n",
+        "Lệnh này rất hữu ích nếu chúng ta đang đánh giá một mô hình và muốn lưu trữ đánh giá của nó với trọng số và cấu hình (`configs`) trên Hugging Face Hub.\n",
         "\n",
         "### `push_to_tensorboard`\n",
         "\n",
-        "This would be useful if we were building an evaluation tool or script, where we wanted to view results within tensorboard."
+        "Lệnh này sẽ hữu ích nếu chúng ta đang xây dựng một công cụ hoặc tập lệnh đánh giá, nơi chúng ta muốn xem kết quả trong `tensorboard`."
       ]
     },
     {
@@ -206,30 +206,30 @@
         "id": "nsNjwzCtltkA"
       },
       "source": [
-        "# Compares models with `lighteval`\n",
+        "# So sánh các mô hình với `lighteval`\n",
         "\n",
-        "We are going to compare two small LLMs on a domain. We will use `Qwen2.5-0.5B` and `SmolLM2-360M-Instruct` and we will evaluate them on a medical domain.\n",
+        "Chúng ta sẽ so sánh hai `LLM` nhỏ trên một lĩnh vực. Chúng ta sẽ sử dụng `Qwen2.5-0.5B` và `SmolLM2-360M-Instruct` và chúng ta sẽ đánh giá chúng trên lĩnh vực y tế.\n",
         "\n",
-        "We can create a domain evaluation from a subset of MMLU evaluations, by defining the evaluation tasks. In lighteval, tasks are described as strings.\n",
+        "Chúng ta có thể tạo một đánh giá lĩnh vực từ một tập hợp con các đánh giá `MMLU`, bằng cách xác định các tác vụ đánh giá. Trong `lighteval`, các tác vụ được mô tả dưới dạng chuỗi.\n",
         "\n",
         "`{suite}|{task}:{subtask}|{num_few_shot}|{0 or 1 to reduce num_few_shot if prompt is too long}`\n",
         "\n",
-        "Therefore, we will pass our list of medicine related tasks like this:\n",
+        "Do đó, chúng ta sẽ chuyển danh sách các tác vụ liên quan đến y học của mình như sau:\n",
         "\n",
         "```\n",
         "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n",
         "```\n",
         "\n",
-        "Which can be translated to :\n",
+        "Có thể được hiểu như sau:\n",
         "\n",
-        "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n",
+        "| Suite | Tác vụ | Số mẫu Fewshot | Giới hạn Fewshots |\n",
         "|---|---|---|---|\n",
         "| leaderboard | mmlu:anatomy | 5 | False |\n",
         "| leaderboard | mmlu:professional_medicine | 5 | False |\n",
         "| leaderboard | mmlu:high_school_biology | 5 | False |\n",
         "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n",
         "\n",
-        "For a full list of lighteval supported tasks. Checkout this page in [the documentation](https://github.com/huggingface/lighteval/wiki/Available-Tasks)."
+        "Để có danh sách đầy đủ các tác vụ được `lighteval` hỗ trợ. Hãy xem trang này trong [tài liệu](https://github.com/huggingface/lighteval/wiki/Available-Tasks)."
       ]
     },
     {
@@ -249,7 +249,7 @@
         "id": "XwcJklSFX4H6"
       },
       "source": [
-        "# Evaluate Qwen2.5 0.5B"
+        "# Đánh giá Qwen2.5 0.5B"
       ]
     },
     {
@@ -716,7 +716,7 @@
         "id": "CIwCaCxJX_hA"
       },
       "source": [
-        "# Evaluate SmolLM 360M"
+        "# Đánh giá SmolLM 360M"
       ]
     },
     {
@@ -877,9 +877,9 @@
         "id": "0HD8aFwSYGHu"
       },
       "source": [
-        "# Visualize Results\n",
+        "# Trực quan hóa kết quả\n",
         "\n",
-        "Now that we have results from the two models we can visualize them side-by-side. We'll keep visualisation simple here, but with this data structure you could represent scores in many ways."
+        "Bây giờ chúng ta đã có kết quả từ hai mô hình, chúng ta có thể trực quan hóa chúng cạnh nhau. Chúng ta sẽ giữ cho việc trực quan hóa đơn giản ở đây, nhưng với cấu trúc dữ liệu này, bạn có thể biểu diễn điểm số theo nhiều cách."
       ]
     },
     {
@@ -909,13 +909,13 @@
         "id": "qJEbQeYDplKX"
       },
       "source": [
-        "# 💐 That's it!\n",
+        "# 💐 Chúc mừng bạn đã hoàn thành!\n",
         "\n",
-        "You have a handy notebook to view model evals. You could use this to:\n",
+        "Bạn đã có một notebook tiện dụng để đánh giá các mô hình. Bạn có thể sử dụng notebook này để:\n",
         "\n",
-        "- select the right model for your inference use case\n",
-        "- evaluate checkpoints during training\n",
-        "- share model scores"
+        "- chọn mô hình phù hợp cho trường hợp sử dụng suy luận của bạn\n",
+        "- đánh giá các điểm kiểm tra trong quá trình đào tạo\n",
+        "- chia sẻ điểm số mô hình"
       ]
     },
     {
@@ -924,9 +924,9 @@
         "id": "jWdS38syaipm"
       },
       "source": [
-        "🏃Next Steps\n",
+        "🏃Các bước tiếp theo\n",
         "\n",
-        "- If you want to go deeper into your evaluation results check out this [notebook](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n"
+        "- Nếu bạn muốn đi sâu hơn vào kết quả đánh giá của mình, hãy xem [sổ tay này](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)"
       ]
     }
   ],
diff --git a/vi/4_evaluation/project/README.md b/vi/4_evaluation/project/README.md
index 98d32eca..4e47e8e5 100644
--- a/vi/4_evaluation/project/README.md
+++ b/vi/4_evaluation/project/README.md
@@ -1,72 +1,72 @@
-# Domain Specific Evaluation with Argilla, Distilabel, and LightEval
+# Đánh Giá Theo Lĩnh Vực Cụ Thể với Argilla, Distilabel, và LightEval
 
-Most popular benchmarks look at very general capabilities (reasoning, math, code), but have you ever needed to study more specific capabilities? 
+Hầu hết các bộ tiêu chuẩn (`benchmark`) phổ biến đều xem xét các khả năng rất chung chung (lý luận, toán học, lập trình), nhưng bạn đã bao giờ cần nghiên cứu các khả năng cụ thể hơn chưa?
 
-What should you do if you need to evaluate a model on a **custom domain** relevant to your use-cases? (For example, financial, legal, medical use cases)  
+Bạn nên làm gì nếu bạn cần đánh giá một mô hình trên một **lĩnh vực cụ thể** liên quan đến các trường hợp sử dụng của bạn? (Ví dụ: các trường hợp sử dụng tài chính, pháp lý, y tế)
 
-This tutorial shows you the full pipeline you can follow, from creating relevant data and annotating your samples to evaluating your model on them, with the easy to use [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), and [lighteval](https://github.com/huggingface/lighteval). For our example, we'll focus on generating exam questions from multiple documents. 
+Hướng dẫn này chỉ cho bạn toàn bộ quy trình (`pipeline`) mà bạn có thể làm theo, từ việc tạo dữ liệu liên quan và chú thích các mẫu của bạn đến việc đánh giá mô hình của bạn trên chúng, với các công cụ dễ sử dụng [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), và [lighteval](https://github.com/huggingface/lighteval). Trong ví dụ của chúng tôi, chúng tôi sẽ tập trung vào việc tạo các câu hỏi đánh giá từ nhiều tài liệu.
 
-## Project Structure
+## Cấu trúc dự án
 
-For our process, we will follow 4 steps, with a script for each: generating a dataset, annotating it, extracting relevant samples for evaluation from it, and actually evaluating models.
+Đối với quy trình của chúng tôi, chúng tôi sẽ làm theo 4 bước, với một tập lệnh cho mỗi bước: tạo tập dữ liệu, chú thích nó, trích xuất các mẫu liên quan để đánh giá từ nó và thực sự đánh giá các mô hình.
 
-| Script Name | Description |
+| Tên tập lệnh | Mô tả |
 |-------------|-------------|
-| generate_dataset.py | Generates exam questions from multiple text documents using a specified language model. |
-| annotate_dataset.py | Creates an Argilla dataset for manual annotation of the generated exam questions. |
-| create_dataset.py | Processes annotated data from Argilla and creates a Hugging Face dataset. |
-| evaluation_task.py | Defines a custom LightEval task for evaluating language models on the exam questions dataset. |
+| generate_dataset.py | Tạo các câu hỏi đánh giá từ nhiều tài liệu văn bản bằng cách sử dụng một mô hình ngôn ngữ được chỉ định. |
+| annotate_dataset.py | Tạo tập dữ liệu Argilla để chú thích thủ công các câu hỏi đánh giá được tạo. |
+| create_dataset.py | Xử lý dữ liệu đã chú thích từ Argilla và tạo tập dữ liệu Hugging Face. |
+| evaluation_task.py | Định nghĩa một tác vụ LightEval tùy chỉnh để đánh giá các mô hình ngôn ngữ trên tập dữ liệu câu hỏi đánh giá. |
 
-## Steps
+## Các bước thực hành
 
-### 1. Generate Dataset
+### 1. Tạo tập dữ liệu
 
-The `generate_dataset.py` script uses the distilabel library to generate exam questions based on multiple text documents. It uses the specified model (default: Meta-Llama-3.1-8B-Instruct) to create questions, correct answers, and incrorect answers (known as distractors). You should add you own data samples and you might wish to use a different model.
+Tập lệnh `generate_dataset.py` sử dụng thư viện `distilabel` để tạo các câu hỏi thi dựa trên nhiều tài liệu văn bản. Nó sử dụng mô hình được chỉ định (mặc định: Meta-Llama-3.1-8B-Instruct) để tạo các câu hỏi, câu trả lời đúng và câu trả lời sai (được gọi là câu gây nhiễu). Bạn nên thêm các mẫu dữ liệu của riêng bạn và bạn có thể muốn sử dụng một mô hình khác.
 
-To run the generation:
+Để chạy quá trình tạo:
 
 ```sh
 python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
 ```
 
-This will create a [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) containing the generated exam questions for all documents in the input directory. 
+Thao tác này sẽ tạo một [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) chứa các câu hỏi thi được tạo cho tất cả các tài liệu trong thư mục đầu vào.
 
-### 2. Annotate Dataset
+### 2. Chú thích tập dữ liệu
 
-The `annotate_dataset.py` script takes the generated questions and creates an Argilla dataset for annotation. It sets up the dataset structure and populates it with the generated questions and answers, randomizing the order of answers to avoid bias. Once in Argilla, you or a domain expert can validate the dataset with the correct answers.
+Tập lệnh `annotate_dataset.py` lấy các câu hỏi đã tạo và tạo tập dữ liệu Argilla để chú thích. Nó thiết lập cấu trúc tập dữ liệu và điền vào đó các câu hỏi và câu trả lời đã tạo, sắp xếp ngẫu nhiên thứ tự các câu trả lời để tránh sai lệch. Khi ở trong Argilla, bạn hoặc một chuyên gia trong lĩnh vực có thể xác thực tập dữ liệu với các câu trả lời đúng.
 
-You will see suggested correct answers from the LLM in random order and you can approve the correct answer or select a different one. The duration of this process will depend on the scale of your evaluation dataset, the complexity of your domain data, and the quality of your LLM. For example, we were able to create 150 samples within 1 hour on the domain of transfer learning, using Llama-3.1-70B-Instruct, mostly by approving the correct answer and discarding the incorrect ones.
+Bạn sẽ thấy các câu trả lời đúng được đề xuất từ `LLM` theo thứ tự ngẫu nhiên và bạn có thể phê duyệt câu trả lời đúng hoặc chọn một câu trả lời khác. Thời gian của quá trình này sẽ phụ thuộc vào quy mô của tập dữ liệu đánh giá của bạn, độ phức tạp của dữ liệu lĩnh vực của bạn và chất lượng của `LLM` của bạn. Ví dụ: chúng tôi đã có thể tạo 150 mẫu trong vòng 1 giờ trên lĩnh vực chuyển giao học tập (`transfer learning`), sử dụng Llama-3.1-70B-Instruct, chủ yếu bằng cách phê duyệt câu trả lời đúng và loại bỏ những câu trả lời không chính xác.
 
-To run the annotation process:
+Để chạy quá trình chú thích:
 
 ```sh
 python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
 ```
 
-This will create an Argilla dataset that can be used for manual review and annotation.
+Thao tác này sẽ tạo một tập dữ liệu Argilla có thể được sử dụng để xem xét và chú thích thủ công.
 
 ![argilla_dataset](./images/domain_eval_argilla_view.png)
 
-If you're not using Argilla, deploy it locally or on spaces following this [quickstart guide](https://docs.argilla.io/latest/getting_started/quickstart/).
+Nếu bạn không sử dụng Argilla, hãy triển khai cục bộ hoặc trên không gian (`spaces`) theo [hướng dẫn bắt đầu nhanh](https://docs.argilla.io/latest/getting_started/quickstart/) này.
 
-### 3. Create Dataset
+### 3. Tạo tập dữ liệu
 
-The `create_dataset.py` script processes the annotated data from Argilla and creates a Hugging Face dataset. It handles both suggested and manually annotated answers. The script will create a dataset with the question, possible answers, and the column name for the correct answer. To create the final dataset:
+Tập lệnh `create_dataset.py` xử lý dữ liệu đã chú thích từ Argilla và tạo tập dữ liệu Hugging Face. Nó xử lý cả các câu trả lời được đề xuất và được chú thích thủ công. Tập lệnh sẽ tạo một tập dữ liệu với câu hỏi, các câu trả lời có thể và tên cột cho câu trả lời đúng. Để tạo tập dữ liệu cuối cùng:
 
 ```sh
 huggingface_hub login
 python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
 ```
 
-This will push the dataset to the Hugging Face Hub under the specified repository. You can view the sample dataset on the hub [here](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), and a preview of the dataset looks like this:
+Thao tác này sẽ đẩy tập dữ liệu lên Hugging Face Hub dưới kho lưu trữ được chỉ định. Bạn có thể xem tập dữ liệu mẫu trên `hub` [tại đây](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train) và bản xem trước của tập dữ liệu trông như thế này:
 
 ![hf_dataset](./images/domain_eval_dataset_viewer.png)
 
-### 4. Evaluation Task
+### 4. Tác vụ đánh giá
 
-The `evaluation_task.py` script defines a custom LightEval task for evaluating language models on the exam questions dataset. It includes a prompt function, a custom accuracy metric, and the task configuration. 
+Tập lệnh `evaluation_task.py` định nghĩa một tác vụ LightEval tùy chỉnh để đánh giá các mô hình ngôn ngữ trên tập dữ liệu câu hỏi thi. Nó bao gồm một hàm nhắc nhở (`prompt function`), một chỉ số chính xác tùy chỉnh và cấu hình tác vụ.
 
-To evaluate a model using lighteval with the custom exam questions task:
+Để đánh giá một mô hình bằng `lighteval` với tác vụ câu hỏi thi tùy chỉnh:
 
 ```sh
 lighteval accelerate \
@@ -76,10 +76,8 @@ lighteval accelerate \
     --output_dir "./evals"
 ```
 
-You can find detailed guides in lighteval wiki about each of these steps: 
-
-- [Creating a Custom Task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
-- [Creating a Custom Metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
-- [Using existing metrics](https://github.com/huggingface/lighteval/wiki/Metric-List)
-
+Bạn có thể tìm thấy các hướng dẫn chi tiết trong `lighteval wiki` về từng bước sau:
 
+- [Tạo tác vụ tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [Tạo chỉ số tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [Sử dụng các chỉ số hiện có](https://github.com/huggingface/lighteval/wiki/Metric-List)
\ No newline at end of file

From 1082595e2a7c46d185643166236a63c9223d65e8 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Wed, 8 Jan 2025 05:36:45 +0100
Subject: [PATCH 26/63] use none string not null

---
 .../notebooks/dpo_finetuning_example.ipynb     | 18 ++++++++----------
 .../notebooks/orpo_finetuning_example.ipynb    |  2 +-
 .../notebooks/finetune_sft_peft.ipynb          |  2 +-
 .../notebooks/dpo_finetuning_example.ipynb     | 18 ++++++++----------
 .../notebooks/orpo_finetuning_example.ipynb    | 11 +++++------
 .../notebooks/finetune_sft_peft.ipynb          |  4 ++--
 .../notebooks/finetune_sft_peft.ipynb          |  4 ++--
 .../notebooks/dpo_finetuning_example.ipynb     |  8 +++-----
 .../notebooks/orpo_finetuning_example.ipynb    |  7 +++----
 .../notebooks/dpo_finetuning_example.ipynb     |  6 ++----
 .../notebooks/orpo_finetuning_example.ipynb    | 13 ++++++-------
 .../notebooks/finetune_sft_peft.ipynb          | 10 +++++-----
 12 files changed, 46 insertions(+), 57 deletions(-)

diff --git a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
index 58333c15..3ddef6a6 100644
--- a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -60,7 +60,7 @@
     "import os\n",
     "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
     "from datasets import load_dataset\n",
-    "from trl import DPOTrainer, DPOConfig\n"
+    "from trl import DPOTrainer, DPOConfig"
    ]
   },
   {
@@ -122,9 +122,7 @@
     "device = (\n",
     "    \"cuda\"\n",
     "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
     ")\n",
     "\n",
     "# Model to fine-tune\n",
@@ -269,7 +267,7 @@
     "    # Use bfloat16 precision for faster training\n",
     "    bf16=True,\n",
     "    # Disable wandb/tensorboard logging\n",
-    "    report_to=None,\n",
+    "    report_to=\"none\",\n",
     "    # Keep all columns in dataset even if not used\n",
     "    remove_unused_columns=False,\n",
     "    # Enable MPS (Metal Performance Shaders) for Mac devices\n",
@@ -282,7 +280,7 @@
     "    # Maximum length of the input prompt in tokens\n",
     "    max_prompt_length=1024,\n",
     "    # Maximum combined length of prompt + response in tokens\n",
-    "    max_length=1536\n",
+    "    max_length=1536,\n",
     ")"
    ]
   },
@@ -303,11 +301,11 @@
     "    processing_class=tokenizer,\n",
     "    # DPO-specific temperature parameter that controls the strength of the preference model\n",
     "    # Lower values (like 0.1) make the model more conservative in following preferences\n",
-    "    #beta=0.1,\n",
+    "    # beta=0.1,\n",
     "    # Maximum length of the input prompt in tokens\n",
-    "    #max_prompt_length=1024,\n",
+    "    # max_prompt_length=1024,\n",
     "    # Maximum combined length of prompt + response in tokens\n",
-    "    #max_length=1536,\n",
+    "    # max_length=1536,\n",
     ")"
    ]
   },
@@ -364,7 +362,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.10"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
index e7303b29..a1ec3a18 100644
--- a/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ b/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
@@ -348,7 +348,7 @@
     "    # Gradual learning rate warmup\n",
     "    warmup_steps=10,\n",
     "    # Disable external logging\n",
-    "    report_to=None,\n",
+    "    report_to=\"none\",\n",
     "    # Where to save model/checkpoints\n",
     "    output_dir=\"./results/\",\n",
     "    # Enable MPS (Metal Performance Shaders) if available\n",
diff --git a/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
index 61c59e39..9028d477 100644
--- a/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ b/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -249,7 +249,7 @@
     "    bf16=True,  # Use bfloat16 precision\n",
     "    # Integration settings\n",
     "    push_to_hub=False,  # Don't push to HuggingFace Hub\n",
-    "    report_to=None,  # Disable external logging\n",
+    "    report_to=\"none\",  # Disable external logging\n",
     ")"
    ]
   },
diff --git a/es/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/es/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
index 976ffc77..8bab8b3c 100644
--- a/es/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ b/es/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -34,7 +34,7 @@
     "\n",
     "login()\n",
     "\n",
-    "# Para conveniencia, puedes crear una variable de entorno que contenga tu token de Hugging Face como HF_TOKEN\n"
+    "# Para conveniencia, puedes crear una variable de entorno que contenga tu token de Hugging Face como HF_TOKEN"
    ]
   },
   {
@@ -60,7 +60,7 @@
     "import os\n",
     "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
     "from datasets import load_dataset\n",
-    "from trl import DPOTrainer, DPOConfig\n"
+    "from trl import DPOTrainer, DPOConfig"
    ]
   },
   {
@@ -121,9 +121,7 @@
     "device = (\n",
     "    \"cuda\"\n",
     "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
     ")\n",
     "\n",
     "# Modelo para afinar\n",
@@ -137,7 +135,7 @@
     "\n",
     "# Establece el nombre para el modelo afinado que se guardará y/o subirá\n",
     "finetune_name = \"SmolLM2-FT-DPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_2\"]\n"
+    "finetune_tags = [\"smol-course\", \"module_2\"]"
    ]
   },
   {
@@ -268,14 +266,14 @@
     "    # Usar precisión bfloat16 para un entrenamiento más rápido\n",
     "    bf16=True,\n",
     "    # Desactivar los registros de wandb/tensorboard\n",
-    "    report_to=None,\n",
+    "    report_to=\"none\",\n",
     "    # Mantener todas las columnas en el dataset incluso si no se utilizan\n",
     "    remove_unused_columns=False,\n",
     "    # Habilitar MPS (Metal Performance Shaders) para dispositivos Mac\n",
     "    use_mps_device=device == \"mps\",\n",
     "    # ID del modelo para cargas al HuggingFace Hub\n",
     "    hub_model_id=finetune_name,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -300,7 +298,7 @@
     "    max_prompt_length=1024,\n",
     "    # Longitud máxima combinada de prompt + respuesta en tokens\n",
     "    max_length=1536,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -317,7 +315,7 @@
     "\n",
     "# Sube el modelo a Hugging Face si el inicio de sesión o HF_TOKEN está configurado\n",
     "if os.getenv(\"HF_TOKEN\"):\n",
-    "    trainer.push_to_hub(tags=finetune_tags)\n"
+    "    trainer.push_to_hub(tags=finetune_tags)"
    ]
   },
   {
diff --git a/es/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/es/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
index d912804f..7872f25d 100644
--- a/es/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ b/es/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
@@ -59,6 +59,7 @@
     "\n",
     "# Autenticación en Hugging Face\n",
     "from huggingface_hub import login\n",
+    "\n",
     "login()"
    ]
   },
@@ -258,9 +259,7 @@
     "device = (\n",
     "    \"cuda\"\n",
     "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
     ")\n",
     "\n",
     "# Modelo para afinar\n",
@@ -274,7 +273,7 @@
     "\n",
     "# Establece el nombre para guardar o subir el modelo afinado\n",
     "finetune_name = \"SmolLM2-FT-DPO\"\n",
-    "finetune_tags = [\"smol-course\", \"module_2\"]\n"
+    "finetune_tags = [\"smol-course\", \"module_2\"]"
    ]
   },
   {
@@ -350,13 +349,13 @@
     "    # Calentamiento gradual de la tasa de aprendizaje\n",
     "    warmup_steps=10,\n",
     "    # Desactivar registro externo\n",
-    "    report_to=None,\n",
+    "    report_to=\"none\",\n",
     "    # Dónde guardar el modelo/puntos de control\n",
     "    output_dir=\"./results/\",\n",
     "    # Habilitar MPS (Metal Performance Shaders) si está disponible\n",
     "    use_mps_device=device == \"mps\",\n",
     "    hub_model_id=finetune_name,\n",
-    ")\n"
+    ")"
    ]
   },
   {
diff --git a/es/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/es/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
index 69871429..60408c89 100644
--- a/es/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ b/es/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -49,7 +49,7 @@
     "\n",
     "login()\n",
     "\n",
-    "# Por comodidad, puedes crear una variable de entorno que contenga tu token de Hugging Face como HF_TOKEN\n"
+    "# Por comodidad, puedes crear una variable de entorno que contenga tu token de Hugging Face como HF_TOKEN"
    ]
   },
   {
@@ -249,7 +249,7 @@
     "    bf16=True,  # Usar precisión bfloat16\n",
     "    # Configuración de integración\n",
     "    push_to_hub=False,  # No subir a HuggingFace\n",
-    "    report_to=None,  # Desactivar registro externo\n",
+    "    report_to=\"none\",  # Desactivar registro externo\n",
     ")"
    ]
   },
diff --git a/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
index 04815633..189ead0e 100644
--- a/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ b/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -250,7 +250,7 @@
     "    bf16=True,  # bfloat16 정밀도 사용\n",
     "    # 통합 설정\n",
     "    push_to_hub=False,  # Hugging Face 허브로 내보내지 않음\n",
-    "    report_to=None,  # 외부 로깅 비활성화\n",
+    "    report_to=\"none\",  # 외부 로깅 비활성화\n",
     ")"
    ]
   },
@@ -284,7 +284,7 @@
     "    packing=True,  # 효율성을 위해 입력 패킹 활성화\n",
     "    dataset_kwargs={\n",
     "        \"add_special_tokens\": False,  # 템플릿에서 추가 토큰 처리\n",
-    "        \"append_concat_token\": False,  # 추가 구분자 없음 \n",
+    "        \"append_concat_token\": False,  # 추가 구분자 없음\n",
     "    },\n",
     ")"
    ]
diff --git a/pt-br/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/pt-br/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
index 8563cd7e..f70dc684 100644
--- a/pt-br/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ b/pt-br/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -60,7 +60,7 @@
     "import os\n",
     "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
     "from datasets import load_dataset\n",
-    "from trl import DPOTrainer, DPOConfig\n"
+    "from trl import DPOTrainer, DPOConfig"
    ]
   },
   {
@@ -122,9 +122,7 @@
     "device = (\n",
     "    \"cuda\"\n",
     "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
     ")\n",
     "\n",
     "# Model to fine-tune\n",
@@ -269,7 +267,7 @@
     "    # Use bfloat16 precision for faster training\n",
     "    bf16=True,\n",
     "    # Disable wandb/tensorboard logging\n",
-    "    report_to=None,\n",
+    "    report_to=\"none\",\n",
     "    # Keep all columns in dataset even if not used\n",
     "    remove_unused_columns=False,\n",
     "    # Enable MPS (Metal Performance Shaders) for Mac devices\n",
diff --git a/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
index 00eeb50c..557e24ef 100644
--- a/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ b/pt-br/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
@@ -58,6 +58,7 @@
     "\n",
     "# Authenticate to Hugging Face\n",
     "from huggingface_hub import login\n",
+    "\n",
     "login()"
    ]
   },
@@ -257,9 +258,7 @@
     "device = (\n",
     "    \"cuda\"\n",
     "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
     ")\n",
     "\n",
     "# Model to fine-tune\n",
@@ -349,7 +348,7 @@
     "    # Gradual learning rate warmup\n",
     "    warmup_steps=10,\n",
     "    # Disable external logging\n",
-    "    report_to=None,\n",
+    "    report_to=\"none\",\n",
     "    # Where to save model/checkpoints\n",
     "    output_dir=\"./results/\",\n",
     "    # Enable MPS (Metal Performance Shaders) if available\n",
diff --git a/vi/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/vi/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
index f1e895d4..d519b6a9 100644
--- a/vi/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ b/vi/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -117,9 +117,7 @@
     "device = (\n",
     "    \"cuda\"\n",
     "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
     ")\n",
     "\n",
     "# Mô hình để tinh chỉnh\n",
@@ -264,7 +262,7 @@
     "    # Sử dụng bfloat16 để tăng tốc huấn luyện\n",
     "    bf16=True,\n",
     "    # Tắt logging wandb/tensorboard\n",
-    "    report_to=None,\n",
+    "    report_to=\"none\",\n",
     "    # Giữ lại tất cả các cột trong dataset kể cả không sử dụng\n",
     "    remove_unused_columns=False,\n",
     "    # Bật MPS cho thiết bị Mac\n",
diff --git a/vi/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/vi/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
index d41e7f40..86959e17 100644
--- a/vi/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
+++ b/vi/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
@@ -53,6 +53,7 @@
     "\n",
     "# Authenticate to Hugging Face\n",
     "from huggingface_hub import login\n",
+    "\n",
     "login()"
    ]
   },
@@ -90,7 +91,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: 🐕 Nếu bộ dữ liệu của bạn không được biểu diễn dưới dạng danh sách hội thoại, \n",
+    "# TODO: 🐕 Nếu bộ dữ liệu của bạn không được biểu diễn dưới dạng danh sách hội thoại,\n",
     "# bạn có thể sử dụng hàm `process_dataset` để chuyển đổi nó."
    ]
   },
@@ -248,9 +249,7 @@
     "device = (\n",
     "    \"cuda\"\n",
     "    if torch.cuda.is_available()\n",
-    "    else \"mps\"\n",
-    "    if torch.backends.mps.is_available()\n",
-    "    else \"cpu\"\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
     ")\n",
     "\n",
     "# Mô hình để tinh chỉnh\n",
@@ -315,7 +314,7 @@
     "    # Learning rate nhỏ để tránh catastrophic forgetting\n",
     "    # Đọc thêm về catastrophic forgetting: https://blog.vinbigdata.org/robot-co-the-hoc-suot-doi-khong/\n",
     "    learning_rate=8e-6,\n",
-    "    # Giảm learning rate tuyến tính trong quá trình huấn luyện  \n",
+    "    # Giảm learning rate tuyến tính trong quá trình huấn luyện\n",
     "    lr_scheduler_type=\"linear\",\n",
     "    # Độ dài tối đa kết hợp của chỉ thị + câu trả lời\n",
     "    max_length=1024,\n",
@@ -333,7 +332,7 @@
     "    # Số epoch huấn luyện\n",
     "    num_train_epochs=1,\n",
     "    # Khi nào chạy đánh giá\n",
-    "    evaluation_strategy=\"steps\", \n",
+    "    evaluation_strategy=\"steps\",\n",
     "    # Đánh giá sau mỗi 20% huấn luyện\n",
     "    eval_steps=0.2,\n",
     "    # Ghi log metrics mỗi step\n",
@@ -341,7 +340,7 @@
     "    # Tăng learning rate dần dần\n",
     "    warmup_steps=10,\n",
     "    # Tắt logging bên ngoài\n",
-    "    report_to=None,\n",
+    "    report_to=\"none\",\n",
     "    # Nơi lưu model/checkpoints\n",
     "    output_dir=\"./results/\",\n",
     "    # Bật MPS (Metal Performance Shaders) nếu có\n",
diff --git a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
index 48d41601..cd70f677 100644
--- a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
+++ b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -49,7 +49,7 @@
     "\n",
     "login()\n",
     "\n",
-    "# Để tiện lợi, hãy tạo một biến môi trường lưu token của bạn như là HF_TOKEN\n"
+    "# Để tiện lợi, hãy tạo một biến môi trường lưu token của bạn như là HF_TOKEN"
    ]
   },
   {
@@ -152,7 +152,7 @@
     ").to(device)\n",
     "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
     "\n",
-    "# Tinh chỉnh định dạng \n",
+    "# Tinh chỉnh định dạng\n",
     "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
     "\n",
     "# Đặt tên cho mô hình tinh chỉnh\n",
@@ -188,7 +188,7 @@
    "source": [
     "from peft import LoraConfig\n",
     "\n",
-    "# TODO: Tinh chỉnh các tham số LoRA \n",
+    "# TODO: Tinh chỉnh các tham số LoRA\n",
     "# r: hạng của ma trận LoRA (thường nằm trong khoảng 4-32) , nhỏ hơn tức là sẽ nén nhiều hơn\n",
     "rank_dimension = 6\n",
     "# lora_alpha: hệ số tỷ lệ cho các lớp LoRA (cao = sự thích nghi mạnh mẽ hơn)\n",
@@ -199,7 +199,7 @@
     "\n",
     "peft_config = LoraConfig(\n",
     "    r=rank_dimension,  # hạng của ma trận LoRA\n",
-    "    lora_alpha=lora_alpha,  # hệ số tỷ lệ cho các lớp LoRA \n",
+    "    lora_alpha=lora_alpha,  # hệ số tỷ lệ cho các lớp LoRA\n",
     "    lora_dropout=lora_dropout,  # tỉ lệ dropout cho các lớp LoRA\n",
     "    bias=\"none\",  # Loại bias cho LoRA, những bias sẽ được cập nhật trong quá trình huấn luyện\n",
     "    target_modules=\"all-linear\",  # Những module mà LoRA sẽ được áp dụng\n",
@@ -250,7 +250,7 @@
     "    bf16=True,  # Sử dụng bfloat16\n",
     "    # Những cài đặt khác\n",
     "    push_to_hub=False,  # Không tải lên HuggingFace Hub\n",
-    "    report_to=None,  # Không gửi kết quả lên các nền tảng khác\n",
+    "    report_to=\"none\",  # Không gửi kết quả lên các nền tảng khác\n",
     ")"
    ]
   },

From d330f73a1a0eb5767514a88f78ab737fd6fe7a65 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Wed, 8 Jan 2025 13:57:27 +0100
Subject: [PATCH 27/63] first commit of agents module

---
 8_agents/README.md              |  36 +++++++++++
 8_agents/code_agents.md         |  68 ++++++++++++++++++++
 8_agents/custom_functions.md    |  69 ++++++++++++++++++++
 8_agents/notebooks/agents.ipynb |  18 ++++++
 8_agents/retrieval_agents.md    | 108 ++++++++++++++++++++++++++++++++
 5 files changed, 299 insertions(+)
 create mode 100644 8_agents/README.md
 create mode 100644 8_agents/code_agents.md
 create mode 100644 8_agents/custom_functions.md
 create mode 100644 8_agents/notebooks/agents.ipynb
 create mode 100644 8_agents/retrieval_agents.md

diff --git a/8_agents/README.md b/8_agents/README.md
new file mode 100644
index 00000000..9af07698
--- /dev/null
+++ b/8_agents/README.md
@@ -0,0 +1,36 @@
+# Agents
+
+AI Agents are autonomous systems that can understand user requests, break them down into steps, and execute actions to accomplish tasks. They combine language models with tools and external functions to interact with their environment. This module covers how to build effective agents using the [`smolagents`](https://github.com/huggingface/smolagents) library, which provides a lightweight framework for creating capable AI agents.
+
+## Module Overview
+
+Building effective agents requires understanding three key components. First, retrieval capabilities allow agents to access and use relevant information from various sources. Second, function calling enables agents to take concrete actions in their environment. Finally, domain-specific knowledge and tooling equips agents for specialized tasks like code manipulation.
+
+## Contents
+
+### 1️⃣ [Retrieval Agents](./retrieval_agents.md)
+
+Retrieval agents combine models with knowledge bases. These agents can search and synthesize information from multiple sources, leveraging vector stores for efficient retrieval and implementing RAG (Retrieval Augmented Generation) patterns. They are great at combining web search with custom knowledge bases while maintaining conversation context through memory systems. The module covers implementation strategies including fallback mechanisms for robust information retrieval.
+
+### 2️⃣ [Code Agents](./code_agents.md)
+
+Code agents are specialized autonomous systems designed for software development tasks. These agents excel at analyzing and generating code, performing automated refactoring, and integrating with development tools. The module covers best practices for building code-focused agents that can understand programming languages, work with build systems, and interact with version control while maintaining high code quality standards.
+
+### 3️⃣ [Custom Functions](./custom_functions.md)
+
+Custom function agents extend basic AI capabilities through specialized function calls. This module explores how to design modular and extensible function interfaces that integrate directly with your application's logic. You'll learn to implement proper validation and error handling while creating reliable function-driven workflows. The focus is on building simple systems where agents can predictably interact with external tools and services.
+
+### Exercise Notebooks
+
+| Title | Description | Exercise | Link | Colab |
+|-------|-------------|----------|------|-------|
+| Building a Research Agent | Create an agent that can perform research tasks using retrieval and custom functions | 🐢 Build a simple RAG agent <br> 🐕 Add custom search functions <br> 🦁 Create a full research assistant | [Notebook](./notebooks/agents.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/8_agents/notebooks/building_research_agent.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## Resources
+
+- [SmoLAgents Documentation](https://huggingface.co/docs/smolagents) - Official docs for the SmoLAgents library
+- [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) - Research paper on agent architectures
+- [Agent Guidelines](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for building reliable agents
+- [LangChain Agents](https://python.langchain.com/docs/modules/agents/) - Additional examples of agent implementations
+- [Function Calling Guide](https://platform.openai.com/docs/guides/function-calling) - Understanding function calling in LLMs
+- [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/) - Guide to implementing effective RAG
\ No newline at end of file
diff --git a/8_agents/code_agents.md b/8_agents/code_agents.md
new file mode 100644
index 00000000..430328e8
--- /dev/null
+++ b/8_agents/code_agents.md
@@ -0,0 +1,68 @@
+# Code Agents
+
+Code agents are specialized autonomous systems that handle coding tasks like analysis, generation, refactoring, and testing. These agents leverage domain knowledge about programming languages, build systems, and version control to enhance software development workflows.
+
+## Why Code Agents?
+
+Code agents accelerate development by automating repetitive tasks while maintaining code quality. They excel at generating boilerplate code, performing systematic refactoring, and identifying potential issues through static analysis. The agents combine retrieval capabilities to access external documentation and repositories with function calling to execute concrete actions like creating files or running tests.
+
+## Building Blocks of a Code Agent
+
+Code agents are built on specialized language models fine-tuned for code understanding. These models are augmented with development tools like linters, formatters, and compilers to interact with real-world environments. Through retrieval techniques, agents maintain contextual awareness by accessing documentation and code histories to align with organizational patterns and standards. Action-oriented functions enable agents to perform concrete tasks such as committing changes or initiating merge requests.
+
+In the following example, we create a code agent that can search the web using DuckDuckGo much like the retrieval agent we built earlier.
+
+```python
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+
+agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
+
+agent.run("How many seconds would it take for a leopard at full speed to run through Pont des Arts?")
+```
+
+In the following example, we create a code agent that can get the travel time between two locations. Here, we use the `@tool` decorator to define a custom function that can be used as a tool.
+
+```python
+from smolagents import CodeAgent, HfApiModel, tool
+
+@tool
+def get_travel_duration(start_location: str, destination_location: str, departure_time: Optional[int] = None) -> str:
+    """Gets the travel time in car between two places.
+    
+    Args:
+        start_location: the place from which you start your ride
+        destination_location: the place of arrival
+        departure_time: the departure time, provide only a `datetime.datetime` if you want to specify this
+    """
+    import googlemaps # All imports are placed within the function, to allow for sharing to Hub.
+    import os
+
+    gmaps = googlemaps.Client(os.getenv("GMAPS_API_KEY"))
+
+    if departure_time is None:
+        from datetime import datetime
+        departure_time = datetime(2025, 1, 6, 11, 0)
+
+    directions_result = gmaps.directions(
+        start_location,
+        destination_location,
+        mode="transit",
+        departure_time=departure_time
+    )
+    return directions_result[0]["legs"][0]["duration"]["text"]
+
+agent = CodeAgent(tools=[get_travel_duration], model=HfApiModel(), additional_authorized_imports=["datetime"])
+
+agent.run("Can you give me a nice one-day trip around Paris with a few locations and the times? Could be in the city or outside, but should fit in one day. I'm travelling only via public transportation.")
+
+```
+
+These examples are just the beginning of what you can do with code agents. You can learn more about how to build code agents in the [SmoLAgents documentation](https://huggingface.co/docs/smolagents).
+
+SmoLAgents provides a lightweight framework for building code agents, with a core implementation of approximately 1,000 lines of code. The framework specializes in agents that write and execute Python code snippets, offering sandboxed execution for security. It supports both open-source and proprietary language models, making it adaptable to various development environments.
+
+## Further Reading
+
+- [SmoLAgents Blog](https://huggingface.co/blog/smolagents) - Introduction to SmoLAgents and code interactions
+- [SmoLAgents: Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for reliable agents
+- [Building Effective Agents - Anthropic](https://www.anthropic.com/research/building-effective-agents) - Agent design principles
diff --git a/8_agents/custom_functions.md b/8_agents/custom_functions.md
new file mode 100644
index 00000000..2730d919
--- /dev/null
+++ b/8_agents/custom_functions.md
@@ -0,0 +1,69 @@
+# Custom Function Agents
+
+Custom Function Agents are AI agents that leverage specialized function calls (or “tools”) to perform tasks. Unlike general-purpose agents, Custom Function Agents focus on powering advanced workflows by integrating directly with your application's logic. For example, you can expose database queries, system commands, or any custom utility as isolated functions for the agent to invoke.
+
+## Why Custom Function Agents?
+
+- **Modular and Extensible**: Instead of building one monolithic agent, you can design individual functions that represent discrete capabilities, making your architecture more extensible.
+- **Fine-Grained Control**: Developers can carefully control the agent’s actions by specifying exactly which functions are available and what parameters they accept.
+- **Improved Reliability**: By structuring each function with clear schemas and validations, you reduce errors and unexpected behaviors.
+
+## Basic Workflow
+
+1. **Identify Functions**  
+   Determine which tasks can be transformed into custom functions (e.g., file I/O, database queries, streaming data processing).
+
+2. **Define the Interface**  
+   Use a function signature or schema that precisely outlines each function’s inputs, outputs, and expected behavior. This enforces strong contracts between your agent and its environment.
+
+3. **Register with the Agent**  
+   Your agent needs to “learn” which functions are available. Typically, you pass metadata describing each function’s interface to the language model or agent framework.
+
+4. **Invoke and Validate**  
+   Once the agent selects a function to call, run the function with the provided arguments and validate the results. If valid, feed the results back to the agent for context to drive subsequent decisions.
+
+## Example
+
+Below is a simplified example demonstrating how custom function calls might look in pseudocode. The objective is to perform a user-defined search and retrieve relevant content:
+
+```python
+# Define a custom function with clear input/output types
+def search_database(query: str) -> list:
+    """
+    Search the database for articles matching the query.
+    
+    Args:
+        query (str): The search query string
+        
+    Returns:
+        list: List of matching article results
+    """
+    try:
+        results = database.search(query)
+        return results
+    except DatabaseError as e:
+        logging.error(f"Database search failed: {e}")
+        return []
+
+# Register the function with the agent
+agent.register_function(
+    name="search_database",
+    function=search_database,
+    description="Searches database for articles matching a query"
+)
+
+# Example usage
+def process_search():
+    query = "Find recent articles on AI"
+    results = agent.invoke("search_database", query)
+    
+    if results:
+        agent.process_results(results)
+    else:
+        logging.info("No results found for query")
+```
+
+## Further Reading
+
+- [SmoLAgents Blog](https://huggingface.co/blog/smolagents) - Learn about the latest advancements in AI agents and how they can be applied to custom function agents.
+- [Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - A comprehensive guide on best practices for developing reliable and effective custom function agents.
\ No newline at end of file
diff --git a/8_agents/notebooks/agents.ipynb b/8_agents/notebooks/agents.ipynb
new file mode 100644
index 00000000..709d82cf
--- /dev/null
+++ b/8_agents/notebooks/agents.ipynb
@@ -0,0 +1,18 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/8_agents/retrieval_agents.md b/8_agents/retrieval_agents.md
new file mode 100644
index 00000000..331ac261
--- /dev/null
+++ b/8_agents/retrieval_agents.md
@@ -0,0 +1,108 @@
+# Building Agentic RAG Systems
+
+Agentic RAG (Retrieval Augmented Generation) combines the power of autonomous agents with knowledge retrieval capabilities. While traditional RAG systems simply use an LLM to answer queries based on retrieved information, agentic RAG takes this further by allowing the system to intelligently control its own retrieval and response process.
+
+Traditional RAG has key limitations - it only performs a single retrieval step and relies on direct semantic similarity with the user query, which can miss relevant information. Agentic RAG addresses these challenges by empowering the agent to formulate its own search queries, critique results, and perform multiple retrieval steps as needed.
+
+## Basic Retrieval with DuckDuckGo
+
+Let's start by building a simple agent that can search the web using DuckDuckGo. This agent will be able to answer questions by retrieving relevant information and synthesizing responses.
+
+```python
+from smolagents import Agent
+from smolagents.tools import DuckDuckGoSearch
+from smolagents.memory import SqliteMemory
+
+# Initialize the search tool
+search_tool = DuckDuckGoSearch()
+
+# Create an agent with memory
+agent = Agent(
+    name="research_assistant",
+    description="I help find and synthesize information from the web",
+    tools=[search_tool],
+    memory=SqliteMemory(db_path="agent_memory.db")
+)
+
+# Example usage
+response = agent.run(
+    "What are the latest developments in fusion energy?"
+)
+print(response)
+```
+
+The agent will:
+1. Analyze the query to determine what information is needed
+2. Use DuckDuckGo to search for relevant content
+3. Synthesize the retrieved information into a coherent response
+4. Store the interaction in its memory for future reference
+
+## Custom Knowledge Base Tool
+
+For domain-specific applications, we often want to combine web search with our own knowledge base. Let's create a custom tool that can query a vector database of technical documentation.
+
+```python
+from smolagents import Agent, Tool
+from smolagents.embeddings import OpenAIEmbeddings
+from smolagents.vectorstores import Qdrant
+
+class DocumentationTool(Tool):
+    def __init__(self, docs_path: str):
+        self.embeddings = OpenAIEmbeddings()
+        self.vectorstore = Qdrant(
+            collection_name="documentation",
+            embeddings=self.embeddings
+        )
+        # Load and index documentation
+        self.load_docs(docs_path)
+    
+    def load_docs(self, path: str):
+        # Load documents and split into chunks
+        documents = self.load_and_split(path)
+        # Index in vector store
+        self.vectorstore.add_documents(documents)
+    
+    def search(self, query: str, k: int = 3):
+        # Retrieve relevant documents
+        results = self.vectorstore.similarity_search(query, k=k)
+        return [doc.page_content for doc in results]
+
+# Create tools
+docs_tool = DocumentationTool("path/to/docs")
+search_tool = DuckDuckGoSearch()
+
+# Initialize agent with both tools
+agent = Agent(
+    name="technical_assistant",
+    description="I help answer questions using documentation and web search",
+    tools=[docs_tool, search_tool],
+    memory=SqliteMemory(db_path="tech_agent_memory.db")
+)
+
+# Example usage
+response = agent.run(
+    "How do I implement RAG in my application? Include both general concepts and specific code examples."
+)
+print(response)
+```
+
+This enhanced agent can:
+1. First check the documentation for relevant information
+2. Fall back to web search if needed
+3. Combine information from both sources
+4. Maintain conversation context through memory
+
+## Enhanced Retrieval Capabilities
+
+When building agentic RAG systems, the agent can employ sophisticated strategies like:
+
+1. Query Reformulation - Instead of using the raw user query, the agent can craft optimized search terms that better match the target documents
+2. Multi-Step Retrieval - The agent can perform multiple searches, using initial results to inform subsequent queries
+3. Source Integration - Information can be combined from multiple sources like web search and local documentation
+4. Result Validation - Retrieved content can be analyzed for relevance and accuracy before being included in responses
+
+Effective agentic RAG systems require careful consideration of several key aspects. The agent should select between available tools based on the query type and context. Memory systems help maintain conversation history and avoid repetitive retrievals. Having fallback strategies ensures the system can still provide value even when primary retrieval methods fail. Additionally, implementing validation steps helps ensure the accuracy and relevance of retrieved information.
+
+## Next Steps
+
+⏩ Check out the [Code Agents](./code_agents.md) module to learn how to build agents that can manipulate code.

From 76e273d1da7f5ee5d26d516a678bd70d7cbe62b0 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Wed, 8 Jan 2025 15:05:47 +0100
Subject: [PATCH 28/63] add exercise notebook

---
 8_agents/notebooks/agents.ipynb | 200 +++++++++++++++++++++++++++++---
 1 file changed, 183 insertions(+), 17 deletions(-)

diff --git a/8_agents/notebooks/agents.ipynb b/8_agents/notebooks/agents.ipynb
index 709d82cf..52bd1afe 100644
--- a/8_agents/notebooks/agents.ipynb
+++ b/8_agents/notebooks/agents.ipynb
@@ -1,18 +1,184 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Building AI Agents\n",
+                "\n",
+                "This notebook contains exercises to help you learn how to build different types of agents using the `smolagents` library. We'll progress from basic to more complex implementations.\n",
+                "\n",
+                "## Setup\n",
+                "\n",
+                "First, let's install the required packages:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "source": [
+                "!pip install smolagents"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## 🐢 Exercise 1: Basic Code Agent\n",
+                "\n",
+                "Let's start by creating a simple code agent that can answer programming-related questions using web search."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "source": [
+                "from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel\n",
+                "\n",
+                "# Initialize the agent\n",
+                "agent = CodeAgent(\n",
+                "    tools=[DuckDuckGoSearchTool()],\n",
+                "    model=HfApiModel()\n",
+                ")\n",
+                "\n",
+                "# Test the agent\n",
+                "response = agent.run(\"What's the difference between a list and a tuple in Python?\")\n",
+                "print(response)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 🤔 Exercise 1 Challenge\n",
+                "Try asking the agent to explain different programming concepts and evaluate its responses. How well does it handle:\n",
+                "1. Basic syntax questions\n",
+                "2. Language-specific features\n",
+                "3. Code examples"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## 🐕 Exercise 2: Agent with Custom Functions\n",
+                "\n",
+                "Now let's create an agent that can perform specific tasks using custom functions. We'll implement a simple calculator tool."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "source": [
+                "from smolagents import CodeAgent, tool\n",
+                "from typing import Union, List\n",
+                "\n",
+                "@tool\n",
+                "def calculate(operation: str, numbers: List[float]) -> float:\n",
+                "    \"\"\"Performs basic mathematical operations on a list of numbers.\n",
+                "    \n",
+                "    Args:\n",
+                "        operation: One of 'sum', 'average', 'multiply', 'min', 'max'\n",
+                "        numbers: List of numbers to operate on\n",
+                "    \n",
+                "    Returns:\n",
+                "        float: Result of the operation\n",
+                "    \"\"\"\n",
+                "    if operation == 'sum':\n",
+                "        return sum(numbers)\n",
+                "    elif operation == 'average':\n",
+                "        return sum(numbers) / len(numbers)\n",
+                "    elif operation == 'multiply':\n",
+                "        result = 1\n",
+                "        for n in numbers:\n",
+                "            result *= n\n",
+                "        return result\n",
+                "    elif operation == 'min':\n",
+                "        return min(numbers)\n",
+                "    elif operation == 'max':\n",
+                "        return max(numbers)\n",
+                "    else:\n",
+                "        raise ValueError(f\"Unknown operation: {operation}\")\n",
+                "\n",
+                "# Create agent with custom tool\n",
+                "math_agent = CodeAgent(\n",
+                "    tools=[calculate],\n",
+                "    model=HfApiModel()\n",
+                ")\n",
+                "\n",
+                "# Test the agent\n",
+                "response = math_agent.run(\"What is the average of 10, 15, 20, 25, and 30?\")\n",
+                "print(response)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 🤔 Exercise 2 Challenge\n",
+                "1. Add more mathematical operations to the calculator tool\n",
+                "2. Create a new custom tool (e.g., for string manipulation or date calculations)\n",
+                "3. Combine multiple custom tools in one agent"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## 🦁 Exercise 3: Advanced Retrieval Agent\n",
+                "\n",
+                "Finally, let's build a more sophisticated agent that combines web search with memory to maintain context across conversations."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "source": [
+                "from smolagents import Agent\n",
+                "from smolagents.tools import DuckDuckGoSearch\n",
+                "from smolagents.memory import SqliteMemory\n",
+                "\n",
+                "# Initialize the agent with memory\n",
+                "research_agent = Agent(\n",
+                "    name=\"research_assistant\",\n",
+                "    description=\"I help find and synthesize information while maintaining context\",\n",
+                "    tools=[DuckDuckGoSearch()],\n",
+                "    memory=SqliteMemory(db_path=\"research_memory.db\")\n",
+                ")\n",
+                "\n",
+                "# Test with a multi-turn conversation\n",
+                "questions = [\n",
+                "    \"What are the main types of machine learning?\",\n",
+                "    \"Can you explain supervised learning in more detail?\",\n",
+                "    \"What are some popular algorithms for this type?\"\n",
+                "]\n",
+                "\n",
+                "for question in questions:\n",
+                "    print(f\"\\nQ: {question}\")\n",
+                "    response = research_agent.run(question)\n",
+                "    print(f\"A: {response}\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### 🤔 Exercise 3 Challenge\n",
+                "1. Test how well the agent maintains context across different topics\n",
+                "2. Implement a custom knowledge base tool (as shown in the retrieval_agents.md example)\n",
+                "3. Create a hybrid agent that combines code understanding with research capabilities"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3",
+            "language": "python",
+            "name": "python3"
+        }
+    }
+}
\ No newline at end of file

From b62eb3a758b75029236c4e2410fa3cd52a8cca37 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Fri, 10 Jan 2025 09:43:31 +0100
Subject: [PATCH 29/63] update agents notebook with exercises

---
 8_agents/notebooks/agents.ipynb | 31 +++++++++++++++++++------------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/8_agents/notebooks/agents.ipynb b/8_agents/notebooks/agents.ipynb
index 52bd1afe..7a09dec6 100644
--- a/8_agents/notebooks/agents.ipynb
+++ b/8_agents/notebooks/agents.ipynb
@@ -17,8 +17,17 @@
             "cell_type": "code",
             "execution_count": null,
             "metadata": {},
+            "outputs": [],
             "source": [
-                "!pip install smolagents"
+                "!pip install smolagents\n",
+                "\n",
+                "# Install the requirements in Google Colab\n",
+                "# !pip install transformers datasets trl huggingface_hub\n",
+                "\n",
+                "# Authenticate to Hugging Face\n",
+                "from huggingface_hub import login\n",
+                "\n",
+                "login()"
             ]
         },
         {
@@ -34,6 +43,7 @@
             "cell_type": "code",
             "execution_count": null,
             "metadata": {},
+            "outputs": [],
             "source": [
                 "from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel\n",
                 "\n",
@@ -72,6 +82,7 @@
             "cell_type": "code",
             "execution_count": null,
             "metadata": {},
+            "outputs": [],
             "source": [
                 "from smolagents import CodeAgent, tool\n",
                 "from typing import Union, List\n",
@@ -137,17 +148,14 @@
             "cell_type": "code",
             "execution_count": null,
             "metadata": {},
+            "outputs": [],
             "source": [
                 "from smolagents import Agent\n",
                 "from smolagents.tools import DuckDuckGoSearch\n",
-                "from smolagents.memory import SqliteMemory\n",
                 "\n",
                 "# Initialize the agent with memory\n",
                 "research_agent = Agent(\n",
-                "    name=\"research_assistant\",\n",
-                "    description=\"I help find and synthesize information while maintaining context\",\n",
-                "    tools=[DuckDuckGoSearch()],\n",
-                "    memory=SqliteMemory(db_path=\"research_memory.db\")\n",
+                "    ... # TODO: Define the agent\n",
                 ")\n",
                 "\n",
                 "# Test with a multi-turn conversation\n",
@@ -157,10 +165,7 @@
                 "    \"What are some popular algorithms for this type?\"\n",
                 "]\n",
                 "\n",
-                "for question in questions:\n",
-                "    print(f\"\\nQ: {question}\")\n",
-                "    response = research_agent.run(question)\n",
-                "    print(f\"A: {response}\")"
+                "# TODO: Test the agent  "
             ]
         },
         {
@@ -180,5 +185,7 @@
             "language": "python",
             "name": "python3"
         }
-    }
-}
\ No newline at end of file
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}

From 685c700b4289e63a6470a127f76f9aa63bc0e69b Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Mon, 13 Jan 2025 10:14:25 +0100
Subject: [PATCH 30/63] Add agents to README

---
 README.md | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 692956c4..88d5ef90 100644
--- a/README.md
+++ b/README.md
@@ -35,6 +35,7 @@ This course provides a practical, hands-on approach to working with small langua
 | [Vision-language Models](./5_vision_language_models) | Adapt multimodal models for vision-language tasks | ✅ Ready | Dec 16, 2024 |
 | [Synthetic Datasets](./6_synthetic_datasets) | Create and validate synthetic datasets for training | ✅ Ready | Dec 20, 2024 |
 | [Inference](./7_inference) | Infer with models efficiently | [🚧 WIP](https://github.com/huggingface/smol-course/pull/150) | Jan 8, 2025 |
+~ [Agents](./8_agents) | Build your own agentic AI | ✅ Ready | Jan 13, 2025 ||
 | Capstone Project | Use what you've learnt to climb the leaderboard! | [🚧 WIP](https://github.com/huggingface/smol-course/pull/97) | Jan 10, 2025 |
 
 ## Why Small Language Models?
@@ -87,8 +88,3 @@ pip install -r requirements.txt
 pip install transformers trl datasets huggingface_hub
 ```
 
-## Engagement
-
-Let's share this, so that loads of people can learn to finetune LLMs without expensive hardware.
-
-[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/smol-course&type=Date)](https://star-history.com/#huggingface/smol-course&Date)

From e2ae7922ac31cf9bfc920a8bb17f8b0617f97bf8 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Mon, 13 Jan 2025 10:15:01 +0100
Subject: [PATCH 31/63] Fix README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 88d5ef90..76152a0d 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ This course provides a practical, hands-on approach to working with small langua
 | [Vision-language Models](./5_vision_language_models) | Adapt multimodal models for vision-language tasks | ✅ Ready | Dec 16, 2024 |
 | [Synthetic Datasets](./6_synthetic_datasets) | Create and validate synthetic datasets for training | ✅ Ready | Dec 20, 2024 |
 | [Inference](./7_inference) | Infer with models efficiently | [🚧 WIP](https://github.com/huggingface/smol-course/pull/150) | Jan 8, 2025 |
-~ [Agents](./8_agents) | Build your own agentic AI | ✅ Ready | Jan 13, 2025 ||
+| [Agents](./8_agents) | Build your own agentic AI | ✅ Ready | Jan 13, 2025 ||
 | Capstone Project | Use what you've learnt to climb the leaderboard! | [🚧 WIP](https://github.com/huggingface/smol-course/pull/97) | Jan 10, 2025 |
 
 ## Why Small Language Models?

From ababad16d6c767346363307385561893e060db76 Mon Sep 17 00:00:00 2001
From: CharlesCNorton <135471798+CharlesCNorton@users.noreply.github.com>
Date: Mon, 13 Jan 2025 07:14:36 -0500
Subject: [PATCH 32/63] Update README.md
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Fix plural subject–verb agreement for “knowledge and tooling”

- **Issue**: The phrase “domain-specific knowledge and tooling” represents a plural subject, but the verb form used (“equips”) is singular.

- **Fix**: Change “equips” to “equip” to correctly reflect the plural subject.

- When two items are joined by “and”—in this case, “knowledge” and “tooling”—they form a plural subject.

- Using “equip” (plural) maintains proper subject–verb agreement.
---
 8_agents/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/8_agents/README.md b/8_agents/README.md
index 9af07698..ef6f95ee 100644
--- a/8_agents/README.md
+++ b/8_agents/README.md
@@ -4,7 +4,7 @@ AI Agents are autonomous systems that can understand user requests, break them d
 
 ## Module Overview
 
-Building effective agents requires understanding three key components. First, retrieval capabilities allow agents to access and use relevant information from various sources. Second, function calling enables agents to take concrete actions in their environment. Finally, domain-specific knowledge and tooling equips agents for specialized tasks like code manipulation.
+Building effective agents requires understanding three key components. First, retrieval capabilities allow agents to access and use relevant information from various sources. Second, function calling enables agents to take concrete actions in their environment. Finally, domain-specific knowledge and tooling equip agents for specialized tasks like code manipulation.
 
 ## Contents
 
@@ -33,4 +33,4 @@ Custom function agents extend basic AI capabilities through specialized function
 - [Agent Guidelines](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for building reliable agents
 - [LangChain Agents](https://python.langchain.com/docs/modules/agents/) - Additional examples of agent implementations
 - [Function Calling Guide](https://platform.openai.com/docs/guides/function-calling) - Understanding function calling in LLMs
-- [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/) - Guide to implementing effective RAG
\ No newline at end of file
+- [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/) - Guide to implementing effective RAG

From bb4542c2ce507b2f361d49a34c240733f8e02b41 Mon Sep 17 00:00:00 2001
From: sergiopaniego <sergiopaniegoblanco@gmail.com>
Date: Mon, 13 Jan 2025 18:54:00 +0100
Subject: [PATCH 33/63] Updated LangChain URL

---
 8_agents/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/8_agents/README.md b/8_agents/README.md
index 9af07698..26572352 100644
--- a/8_agents/README.md
+++ b/8_agents/README.md
@@ -31,6 +31,6 @@ Custom function agents extend basic AI capabilities through specialized function
 - [SmoLAgents Documentation](https://huggingface.co/docs/smolagents) - Official docs for the SmoLAgents library
 - [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) - Research paper on agent architectures
 - [Agent Guidelines](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for building reliable agents
-- [LangChain Agents](https://python.langchain.com/docs/modules/agents/) - Additional examples of agent implementations
+- [LangChain Agents](https://python.langchain.com/docs/how_to/#agents) - Additional examples of agent implementations
 - [Function Calling Guide](https://platform.openai.com/docs/guides/function-calling) - Understanding function calling in LLMs
 - [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/) - Guide to implementing effective RAG
\ No newline at end of file

From f9c74d2dfc4cad7fa4a31d7ee5b6796a985d072f Mon Sep 17 00:00:00 2001
From: sergiopaniego <sergiopaniegoblanco@gmail.com>
Date: Mon, 13 Jan 2025 19:07:09 +0100
Subject: [PATCH 34/63] Updated SmolAgents to smolagents

---
 8_agents/README.md           | 2 +-
 8_agents/code_agents.md      | 8 ++++----
 8_agents/custom_functions.md | 2 +-
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/8_agents/README.md b/8_agents/README.md
index 26572352..46610e78 100644
--- a/8_agents/README.md
+++ b/8_agents/README.md
@@ -28,7 +28,7 @@ Custom function agents extend basic AI capabilities through specialized function
 
 ## Resources
 
-- [SmoLAgents Documentation](https://huggingface.co/docs/smolagents) - Official docs for the SmoLAgents library
+- [smolagents Documentation](https://huggingface.co/docs/smolagents) - Official docs for the smolagents library
 - [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) - Research paper on agent architectures
 - [Agent Guidelines](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for building reliable agents
 - [LangChain Agents](https://python.langchain.com/docs/how_to/#agents) - Additional examples of agent implementations
diff --git a/8_agents/code_agents.md b/8_agents/code_agents.md
index 430328e8..62d89a22 100644
--- a/8_agents/code_agents.md
+++ b/8_agents/code_agents.md
@@ -57,12 +57,12 @@ agent.run("Can you give me a nice one-day trip around Paris with a few locations
 
 ```
 
-These examples are just the beginning of what you can do with code agents. You can learn more about how to build code agents in the [SmoLAgents documentation](https://huggingface.co/docs/smolagents).
+These examples are just the beginning of what you can do with code agents. You can learn more about how to build code agents in the [smolagents documentation](https://huggingface.co/docs/smolagents).
 
-SmoLAgents provides a lightweight framework for building code agents, with a core implementation of approximately 1,000 lines of code. The framework specializes in agents that write and execute Python code snippets, offering sandboxed execution for security. It supports both open-source and proprietary language models, making it adaptable to various development environments.
+smolagents provides a lightweight framework for building code agents, with a core implementation of approximately 1,000 lines of code. The framework specializes in agents that write and execute Python code snippets, offering sandboxed execution for security. It supports both open-source and proprietary language models, making it adaptable to various development environments.
 
 ## Further Reading
 
-- [SmoLAgents Blog](https://huggingface.co/blog/smolagents) - Introduction to SmoLAgents and code interactions
-- [SmoLAgents: Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for reliable agents
+- [smolagents Blog](https://huggingface.co/blog/smolagents) - Introduction to smolagents and code interactions
+- [smolagents: Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - Best practices for reliable agents
 - [Building Effective Agents - Anthropic](https://www.anthropic.com/research/building-effective-agents) - Agent design principles
diff --git a/8_agents/custom_functions.md b/8_agents/custom_functions.md
index 2730d919..ea590ca5 100644
--- a/8_agents/custom_functions.md
+++ b/8_agents/custom_functions.md
@@ -65,5 +65,5 @@ def process_search():
 
 ## Further Reading
 
-- [SmoLAgents Blog](https://huggingface.co/blog/smolagents) - Learn about the latest advancements in AI agents and how they can be applied to custom function agents.
+- [smolagents Blog](https://huggingface.co/blog/smolagents) - Learn about the latest advancements in AI agents and how they can be applied to custom function agents.
 - [Building Good Agents](https://huggingface.co/docs/smolagents/tutorials/building_good_agents) - A comprehensive guide on best practices for developing reliable and effective custom function agents.
\ No newline at end of file

From 730f3128287389214980a1208b3fe1258b632e43 Mon Sep 17 00:00:00 2001
From: sergiopaniego <sergiopaniegoblanco@gmail.com>
Date: Mon, 13 Jan 2025 19:07:47 +0100
Subject: [PATCH 35/63] Updated Colab link

---
 8_agents/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/8_agents/README.md b/8_agents/README.md
index 46610e78..6e58278b 100644
--- a/8_agents/README.md
+++ b/8_agents/README.md
@@ -24,7 +24,7 @@ Custom function agents extend basic AI capabilities through specialized function
 
 | Title | Description | Exercise | Link | Colab |
 |-------|-------------|----------|------|-------|
-| Building a Research Agent | Create an agent that can perform research tasks using retrieval and custom functions | 🐢 Build a simple RAG agent <br> 🐕 Add custom search functions <br> 🦁 Create a full research assistant | [Notebook](./notebooks/agents.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/8_agents/notebooks/building_research_agent.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| Building a Research Agent | Create an agent that can perform research tasks using retrieval and custom functions | 🐢 Build a simple RAG agent <br> 🐕 Add custom search functions <br> 🦁 Create a full research assistant | [Notebook](./notebooks/agents.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/8_agents/notebooks/agents.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
 
 ## Resources
 

From 155c059153be729ed51b6e7382197d2c2dfa3a57 Mon Sep 17 00:00:00 2001
From: sergiopaniego <sergiopaniegoblanco@gmail.com>
Date: Mon, 13 Jan 2025 19:08:33 +0100
Subject: [PATCH 36/63] Updated type hint in calculate function

---
 8_agents/notebooks/agents.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/8_agents/notebooks/agents.ipynb b/8_agents/notebooks/agents.ipynb
index 7a09dec6..63dfc0cd 100644
--- a/8_agents/notebooks/agents.ipynb
+++ b/8_agents/notebooks/agents.ipynb
@@ -85,10 +85,10 @@
             "outputs": [],
             "source": [
                 "from smolagents import CodeAgent, tool\n",
-                "from typing import Union, List\n",
+                "from typing import Union\n",
                 "\n",
                 "@tool\n",
-                "def calculate(operation: str, numbers: List[float]) -> float:\n",
+                "def calculate(operation: str, numbers: object) -> float:\n",
                 "    \"\"\"Performs basic mathematical operations on a list of numbers.\n",
                 "    \n",
                 "    Args:\n",

From c3ea7043199e726305ef33111e4a3283b33b4047 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Mon, 13 Jan 2025 19:12:17 +0100
Subject: [PATCH 37/63] mistakes in retrievers

---
 8_agents/retrieval_agents.md | 97 ++++++++++++++++++++----------------
 1 file changed, 55 insertions(+), 42 deletions(-)

diff --git a/8_agents/retrieval_agents.md b/8_agents/retrieval_agents.md
index 331ac261..fcd70d28 100644
--- a/8_agents/retrieval_agents.md
+++ b/8_agents/retrieval_agents.md
@@ -11,7 +11,6 @@ Let's start by building a simple agent that can search the web using DuckDuckGo.
 ```python
 from smolagents import Agent
 from smolagents.tools import DuckDuckGoSearch
-from smolagents.memory import SqliteMemory
 
 # Initialize the search tool
 search_tool = DuckDuckGoSearch()
@@ -20,8 +19,7 @@ search_tool = DuckDuckGoSearch()
 agent = Agent(
     name="research_assistant",
     description="I help find and synthesize information from the web",
-    tools=[search_tool],
-    memory=SqliteMemory(db_path="agent_memory.db")
+    tools=[search_tool]
 )
 
 # Example usage
@@ -42,48 +40,39 @@ The agent will:
 For domain-specific applications, we often want to combine web search with our own knowledge base. Let's create a custom tool that can query a vector database of technical documentation.
 
 ```python
-from smolagents import Agent, Tool
-from smolagents.embeddings import OpenAIEmbeddings
-from smolagents.vectorstores import Qdrant
-
-class DocumentationTool(Tool):
-    def __init__(self, docs_path: str):
-        self.embeddings = OpenAIEmbeddings()
-        self.vectorstore = Qdrant(
-            collection_name="documentation",
-            embeddings=self.embeddings
+from smolagents import Tool
+
+class RetrieverTool(Tool):
+    name = "retriever"
+    description = "Uses semantic search to retrieve the parts of transformers documentation that could be most relevant to answer your query."
+    inputs = {
+        "query": {
+            "type": "string",
+            "description": "The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.",
+        }
+    }
+    output_type = "string"
+
+    def __init__(self, docs, **kwargs):
+        super().__init__(**kwargs)
+        self.retriever = BM25Retriever.from_documents(
+            docs, k=10
         )
-        # Load and index documentation
-        self.load_docs(docs_path)
-    
-    def load_docs(self, path: str):
-        # Load documents and split into chunks
-        documents = self.load_and_split(path)
-        # Index in vector store
-        self.vectorstore.add_documents(documents)
-    
-    def search(self, query: str, k: int = 3):
-        # Retrieve relevant documents
-        results = self.vectorstore.similarity_search(query, k=k)
-        return [doc.page_content for doc in results]
-
-# Create tools
-docs_tool = DocumentationTool("path/to/docs")
-search_tool = DuckDuckGoSearch()
 
-# Initialize agent with both tools
-agent = Agent(
-    name="technical_assistant",
-    description="I help answer questions using documentation and web search",
-    tools=[docs_tool, search_tool],
-    memory=SqliteMemory(db_path="tech_agent_memory.db")
-)
+    def forward(self, query: str) -> str:
+        assert isinstance(query, str), "Your search query must be a string"
 
-# Example usage
-response = agent.run(
-    "How do I implement RAG in my application? Include both general concepts and specific code examples."
-)
-print(response)
+        docs = self.retriever.invoke(
+            query,
+        )
+        return "\nRetrieved documents:\n" + "".join(
+            [
+                f"\n\n===== Document {str(i)} =====\n" + doc.page_content
+                for i, doc in enumerate(docs)
+            ]
+        )
+
+retriever_tool = RetrieverTool(docs_processed)
 ```
 
 This enhanced agent can:
@@ -103,6 +92,30 @@ When building agentic RAG systems, the agent can employ sophisticated strategies
 
 Effective agentic RAG systems require careful consideration of several key aspects. The agent should select between available tools based on the query type and context. Memory systems help maintain conversation history and avoid repetitive retrievals. Having fallback strategies ensures the system can still provide value even when primary retrieval methods fail. Additionally, implementing validation steps helps ensure the accuracy and relevance of retrieved information.
 
+```python
+import datasets
+from langchain.docstore.document import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.retrievers import BM25Retriever
+
+knowledge_base = datasets.load_dataset("m-ric/huggingface_doc", split="train")
+knowledge_base = knowledge_base.filter(lambda row: row["source"].startswith("huggingface/transformers"))
+
+source_docs = [
+    Document(page_content=doc["text"], metadata={"source": doc["source"].split("/")[1]})
+    for doc in knowledge_base
+]
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=500,
+    chunk_overlap=50,
+    add_start_index=True,
+    strip_whitespace=True,
+    separators=["\n\n", "\n", ".", " ", ""],
+)
+docs_processed = text_splitter.split_documents(source_docs)
+```
+
 ## Next Steps
 
 ⏩ Check out the [Code Agents](./code_agents.md) module to learn how to build agents that can manipulate code.

From 3f9d3c719bccc4842a65c3c50794243043f164c9 Mon Sep 17 00:00:00 2001
From: Ikko Ashimine <ashimine_ikko_bp@tenso.com>
Date: Wed, 15 Jan 2025 18:46:34 +0900
Subject: [PATCH 38/63] [TRANSLATION] Add 1_instruction_tuning and
 2_preference_alignment EN-JA translation

---
 ja/1_instruction_tuning/README.md             |  30 ++
 ja/1_instruction_tuning/chat_templates.md     | 115 +++++++
 .../notebooks/.env.example                    |   2 +
 .../notebooks/chat_templates_example.ipynb    | 280 ++++++++++++++++++
 .../notebooks/sft_finetuning_example.ipynb    | 280 ++++++++++++++++++
 .../supervised_fine_tuning.md                 |  41 +++
 ja/2_preference_alignment/README.md           |  39 +++
 ja/2_preference_alignment/dpo.md              |  73 +++++
 .../notebooks/dpo_finetuning_example.ipynb    | 167 +++++++++++
 .../notebooks/orpo_finetuning_example.ipynb   | 167 +++++++++++
 ja/2_preference_alignment/orpo.md             |  83 ++++++
 11 files changed, 1277 insertions(+)
 create mode 100644 ja/1_instruction_tuning/README.md
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 create mode 100644 ja/1_instruction_tuning/notebooks/chat_templates_example.ipynb
 create mode 100644 ja/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
 create mode 100644 ja/1_instruction_tuning/supervised_fine_tuning.md
 create mode 100644 ja/2_preference_alignment/README.md
 create mode 100644 ja/2_preference_alignment/dpo.md
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diff --git a/ja/1_instruction_tuning/README.md b/ja/1_instruction_tuning/README.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,30 @@
+# インストラクションチューニング
+
+このモジュールでは、言語モデルのインストラクションチューニングのプロセスをガイドします。インストラクションチューニングとは、特定のタスクに対してモデルを適応させるために、特定のタスクに関連するデータセットで追加のトレーニングを行うことを指します。このプロセスは、特定のタスクにおけるモデルのパフォーマンスを向上させるのに役立ちます。
+
+このモジュールでは、2つのトピックを探ります：1) チャットテンプレートと2) 教師あり微調整
+
+## 1️⃣ チャットテンプレート
+
+チャットテンプレートは、ユーザーとAIモデル間のインタラクションを構造化し、一貫性のある文脈に適した応答を保証します。これらのテンプレートには、システムメッセージや役割に基づくメッセージなどのコンポーネントが含まれます。詳細については、[チャットテンプレート](./chat_templates.md)セクションを参照してください。
+
+## 2️⃣ 教師あり微調整
+
+教師あり微調整（SFT）は、事前トレーニングされた言語モデルを特定のタスクに適応させるための重要なプロセスです。これは、ラベル付きの例を含む特定のタスクのデータセットでモデルをトレーニングすることを含みます。SFTの詳細なガイド、重要なステップ、およびベストプラクティスについては、[教師あり微調整](./supervised_fine_tuning.md)ページを参照してください。
+
+## 演習ノートブック
+
+| タイトル | 説明 | 演習 | リンク | Colab |
+|--------|-------------|-----------|--------|-------|
+| チャットテンプレート | SmolLM2を使用してチャットテンプレートを使用し、チャットml形式のデータセットを処理する方法を学びます | 🐢 `HuggingFaceTB/smoltalk`データセットをchatml形式に変換 <br> 🐕 `openai/gsm8k`データセットをchatml形式に変換 | [ノートブック](./notebooks/chat_templates_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/chat_templates_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| 教師あり微調整 | SFTTrainerを使用してSmolLM2を微調整する方法を学びます | 🐢 `HuggingFaceTB/smoltalk`データセットを使用 <br> 🐕 `bigcode/the-stack-smol`データセットを試す <br> 🦁 実際の使用ケースに関連するデータセットを選択 | [ノートブック](./notebooks/sft_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## 参考文献
+
+- [Transformersのチャットテンプレートに関するドキュメント](https://huggingface.co/docs/transformers/main/en/chat_templating)
+- [TRLの教師あり微調整スクリプト](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)
+- [TRLの`SFTTrainer`](https://huggingface.co/docs/trl/main/en/sft_trainer)
+- [直接選好最適化に関する論文](https://arxiv.org/abs/2305.18290)
+- [TRLを使用した教師あり微調整](https://huggingface.co/docs/trl/main/en/tutorials/supervised_fine_tuning)
+- [ChatMLとHugging Face TRLを使用したGoogle Gemmaの微調整方法](https://www.philschmid.de/fine-tune-google-gemma)
+- [LLMを微調整してペルシャ語の商品カタログをJSON形式で生成する方法](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)
diff --git a/ja/1_instruction_tuning/chat_templates.md b/ja/1_instruction_tuning/chat_templates.md
new file mode 100644
index 00000000..1d92e7f6
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+++ b/ja/1_instruction_tuning/chat_templates.md
@@ -0,0 +1,115 @@
+# チャットテンプレート
+
+チャットテンプレートは、言語モデルとユーザー間のインタラクションを構造化するために不可欠です。これらは会話の一貫した形式を提供し、モデルが各メッセージの文脈と役割を理解し、適切な応答パターンを維持することを保証します。
+
+## ベースモデル vs インストラクションモデル
+
+ベースモデルは次のトークンを予測するために生のテキストデータでトレーニングされる一方、インストラクションモデルは特定の指示に従い会話に参加するように微調整されたモデルです。例えば、`SmolLM2-135M`はベースモデルであり、`SmolLM2-135M-Instruct`はその指示に特化したバリアントです。
+
+ベースモデルをインストラクションモデルのように動作させるためには、モデルが理解できるようにプロンプトを一貫してフォーマットする必要があります。ここでチャットテンプレートが役立ちます。ChatMLは、システム、ユーザー、アシスタントの役割を明確に示すテンプレート形式で会話を構造化します。
+
+ベースモデルは異なるチャットテンプレートで微調整される可能性があるため、インストラクションモデルを使用する際には、正しいチャットテンプレートを使用していることを確認する必要があります。
+
+## チャットテンプレートの理解
+
+チャットテンプレートの核心は、言語モデルと通信する際に会話がどのようにフォーマットされるべきかを定義することです。これには、システムレベルの指示、ユーザーメッセージ、およびアシスタントの応答が含まれ、モデルが理解できる構造化された形式で提供されます。この構造は、インタラクションの一貫性を維持し、モデルがさまざまな種類の入力に適切に応答することを保証します。以下はチャットテンプレートの例です：
+
+```sh
+<|im_end|>ユーザー
+こんにちは！<|im_end|>
+<|im_end|>アシスタント
+はじめまして！<|im_end|>
+<|im_end|>ユーザー
+質問してもいいですか？<|im_end|>
+<|im_end|>アシスタント
+```
+
+`transformers`ライブラリは、モデルのトークナイザーに関連してチャットテンプレートを自動的に処理します。`transformers`でチャットテンプレートがどのように構築されるかについて詳しくは[こちら](https://huggingface.co/docs/transformers/en/chat_templating#how-do-i-use-chat-templates)を参照してください。私たちはメッセージを正しい形式で構造化するだけで、残りはトークナイザーが処理します。以下は基本的な会話の例です：
+
+```python
+messages = [
+    {"role": "system", "content": "あなたは技術的なトピックに焦点を当てた役立つアシスタントです。"},
+    {"role": "user", "content": "チャットテンプレートとは何か説明できますか？"},
+    {"role": "assistant", "content": "チャットテンプレートは、ユーザーとAIモデル間の会話を構造化します..."}
+]
+```
+
+上記の例を分解して、チャットテンプレート形式にどのようにマッピングされるかを見てみましょう。
+
+## システムメッセージ
+
+システムメッセージは、モデルの動作の基礎を設定します。これらは、以降のすべてのインタラクションに影響を与える持続的な指示として機能します。例えば：
+
+```python
+system_message = {
+    "role": "system",
+    "content": "あなたはプロフェッショナルなカスタマーサービスエージェントです。常に礼儀正しく、明確で、役立つようにしてください。"
+}
+```
+
+## 会話
+
+チャットテンプレートは、ユーザーとアシスタント間の以前のやり取りを保存し、会話の履歴を通じて文脈を維持します。これにより、複数ターンにわたる一貫した会話が可能になります：
+
+```python
+conversation = [
+    {"role": "user", "content": "注文に関して助けが必要です"},
+    {"role": "assistant", "content": "お手伝いします。注文番号を教えていただけますか？"},
+    {"role": "user", "content": "注文番号はORDER-123です"},
+]
+```
+
+## Transformersを使用した実装
+
+`transformers`ライブラリは、チャットテンプレートのための組み込みサポートを提供します。使用方法は以下の通りです：
+
+```python
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
+
+messages = [
+    {"role": "system", "content": "あなたは役立つプログラミングアシスタントです。"},
+    {"role": "user", "content": "リストをソートするPython関数を書いてください"},
+]
+
+# チャットテンプレートを適用
+formatted_chat = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+```
+
+## カスタムフォーマット
+
+異なる役割に対して特別なトークンやフォーマットを追加するなど、さまざまなメッセージタイプのフォーマットをカスタマイズできます。例えば：
+
+```python
+template = """
+<|system|>{system_message}
+<|user|>{user_message}
+<|assistant|>{assistant_message}
+""".lstrip()
+```
+
+## マルチターン会話のサポート
+
+テンプレートは、文脈を維持しながら複雑なマルチターン会話を処理できます：
+
+```python
+messages = [
+    {"role": "system", "content": "あなたは数学の家庭教師です。"},
+    {"role": "user", "content": "微積分とは何ですか？"},
+    {"role": "assistant", "content": "微積分は数学の一分野です..."},
+    {"role": "user", "content": "例を教えてください。"},
+]
+```
+
+⏭️ [次へ: Supervised Fine-Tuning](./supervised_fine_tuning.md)
+
+## リソース
+
+- [Hugging Face Chat Templating Guide](https://huggingface.co/docs/transformers/main/en/chat_templating)
+- [Transformers Documentation](https://huggingface.co/docs/transformers)
+- [Chat Templates Examples Repository](https://github.com/chujiezheng/chat_templates)
diff --git a/ja/1_instruction_tuning/notebooks/.env.example b/ja/1_instruction_tuning/notebooks/.env.example
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,2 @@
+# このファイルを.envにコピーし、シークレット値を記入します
+HF_TOKEN=
diff --git a/ja/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/ja/1_instruction_tuning/notebooks/chat_templates_example.ipynb
new file mode 100644
index 00000000..bde66dc4
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+++ b/ja/1_instruction_tuning/notebooks/chat_templates_example.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SFTTrainerを使用した教師あり微調整\n",
+    "\n",
+    "このノートブックでは、`trl`ライブラリの`SFTTrainer`を使用して`HuggingFaceTB/SmolLM2-135M`モデルを微調整する方法を示します。ノートブックのセルを実行すると、モデルの微調整が行われます。さまざまなデータセットを試して、難易度を選択できます。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>演習: SFTTrainerを使用したSmolLM2の微調整</h2>\n",
+    "    <p>Hugging Faceのリポジトリからデータセットを取得し、それを使用してモデルを微調整します。</p>\n",
+    "    <p><b>難易度レベル</b></p>\n",
+    "    <p>🐢 `HuggingFaceTB/smoltalk`データセットを使用</p>\n",
+    "    <p>🐕 `bigcode/the-stack-smol`データセットを試し、特定のサブセット`data/python`でコード生成モデルを微調整します。</p>\n",
+    "    <p>🦁 実際の使用ケースに関連するデータセットを選択します。</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件のインストール\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "\n",
+    "# Hugging Faceへの認証\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# 便利のため、Hugging Faceのトークンを.envファイルのHF_TOKENとして環境変数に設定できます"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 必要なライブラリをインポート\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "# デバイスを動的に設定\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# モデルとトークナイザーをロード\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# チャット形式を設定\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# 微調整されたモデルを保存および/またはアップロードするための名前を設定\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ベースモデルでの生成\n",
+    "\n",
+    "ここでは、チャットテンプレートを持たないベースモデルを試してみます。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# トレーニング前のベースモデルを試してみましょう\n",
+    "prompt = \"プログラミングについての俳句を書いてください\"\n",
+    "\n",
+    "# テンプレートでフォーマット\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "# 応答を生成\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
+    "print(\"トレーニング前:\")\n",
+    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## データセットの準備\n",
+    "\n",
+    "例としてデータセットをロードし、トレーニング用にフォーマットします。データセットは、各入力がプロンプトで、出力がモデルの期待される応答である入力-出力ペアで構成されている必要があります。\n",
+    "\n",
+    "**TRLは、モデルのチャットテンプレートに従って入力メッセージをフォーマットします。** これらは、`role`と`content`キーを持つ辞書のリストとして表現される必要があります。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 例としてデータセットをロード\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: パスと名前のパラメータを使用してデータセットと設定を定義\n",
+    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: 🦁 データセットがTRLでチャットテンプレートに変換できない形式の場合、処理が必要です。 [モジュール](../chat_templates.md)を参照してください。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SFTTrainerの設定\n",
+    "\n",
+    "`SFTTrainer`は、トレーニングプロセスを制御するさまざまなパラメータで設定されます。これには、トレーニングステップの数、バッチサイズ、学習率、および評価戦略が含まれます。これらのパラメータを特定の要件と計算リソースに応じて調整します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SFTTrainerを設定\n",
+    "sft_config = SFTConfig(\n",
+    "    output_dir=\"./sft_output\",  # トレーニング結果の出力ディレクトリ\n",
+    "    max_steps=1000,  # データセットのサイズとトレーニングの所要時間に応じて調整\n",
+    "    per_device_train_batch_size=4,  # GPUのメモリ容量に応じて調整\n",
+    "    learning_rate=5e-5,  # 微調整の一般的な開始点\n",
+    "    logging_steps=10,  # トレーニングメトリックのログ記録頻度\n",
+    "    save_steps=100,  # モデルのチェックポイント保存頻度\n",
+    "    evaluation_strategy=\"steps\",  # 定期的な間隔でモデルを評��\n",
+    "    eval_steps=50,  # 評価の頻度\n",
+    "    use_mps_device=(\n",
+    "        True if device == \"mps\" else False\n",
+    "    ),  # 混合精度トレーニングにMPSを使用\n",
+    "    hub_model_id=finetune_name,  # モデルに一意の名前を付ける\n",
+    ")\n",
+    "\n",
+    "# SFTTrainerを初期化\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=sft_config,\n",
+    "    train_dataset=ds[\"train\"],\n",
+    "    tokenizer=tokenizer,\n",
+    "    eval_dataset=ds[\"test\"],\n",
+    ")\n",
+    "\n",
+    "# TODO: 🦁 🐕 選択したデータセットに合わせてSFTTrainerのパラメータを調整します。例えば、`bigcode/the-stack-smol`データセットを使用する場合、`content`列を選択する必要があります。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## モデルのトレーニング\n",
+    "\n",
+    "トレーナーが設定されたので、モデルのトレーニングを進めることができます。トレーニングプロセスは、データセットを反復し、損失を計算し、この損失を最小化するためにモデルのパラメータを更新することを含みます。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# モデルをトレーニング\n",
+    "trainer.train()\n",
+    "\n",
+    "# モデルを保存\n",
+    "trainer.save_model(f\"./{finetune_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(tags=finetune_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>追加の演習: 微調整されたモデルでの生成</h2>\n",
+    "    <p>🐕 微調整されたモデルを使用して、ベースモデルの例と同じように応答を生成します。</p>\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 同じメッセージで微調整されたモデルをテスト\n",
+    "\n",
+    "# トレーニング前のベースモデルを試してみましょう\n",
+    "prompt = \"プログラミングについての俳句を書いてください\"\n",
+    "\n",
+    "# テンプレートでフォーマット\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "# 応答を生成\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "\n",
+    "# TODO: 微調整されたモデルを使用して、ベースモデルの例と同じように応答を生成します。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完了しました！\n",
+    "\n",
+    "このノートブックでは、`SFTTrainer`を使用して`HuggingFaceTB/SmolLM2-135M`モデルを微調整するためのステップバイステップガイドを提供しました。これらの手順に従うことで、特定のタスクをより効果的に実行するためにモデルを適応させることができます。このコースを続けて作業したい場合、以下の提案を試してみてください：\n",
+    "\n",
+    "- より高い難易度レベルでこのノートブックを試してみてください。\n",
+    "- 同僚のPRをレビューしてください。\n",
+    "- IssueやPRを通じてコースの資料を改善してください。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ja/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/ja/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
new file mode 100644
index 00000000..c1161abe
--- /dev/null
+++ b/ja/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SFTTrainerを使用した教師あり微調整\n",
+    "\n",
+    "このノートブックでは、`trl`ライブラリの`SFTTrainer`を使用して`HuggingFaceTB/SmolLM2-135M`モデルを微調整する方法を示します。ノートブックのセルを実行すると、モデルの微調整が行われます。さまざまなデータセットを試して、難易度を選択できます。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>演習: SFTTrainerを使用したSmolLM2の微調整</h2>\n",
+    "    <p>Hugging Faceのリポジトリからデータセットを取得し、それを使用してモデルを微調整します。</p>\n",
+    "    <p><b>難易度レベル</b></p>\n",
+    "    <p>🐢 `HuggingFaceTB/smoltalk`データセットを使用</p>\n",
+    "    <p>🐕 `bigcode/the-stack-smol`データセットを試し、特定のサブセット`data/python`でコード生成モデルを微調整します。</p>\n",
+    "    <p>🦁 実際の使用ケースに関連するデータセットを選択します。</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件のインストール\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "\n",
+    "# Hugging Faceへの認証\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# 便利のため、Hugging Faceのトークンを.envファイルのHF_TOKENとして環境変数に設定できます"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 必要なライブラリをインポート\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "# デバイスを動的に設定\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# モデルとトークナイザーをロード\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# チャット形式を設定\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# 微調整されたモデルを保存および/またはアップロードするための名前を設定\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ベースモデルでの生成\n",
+    "\n",
+    "ここでは、チャットテンプレートを持たないベースモデルを試してみます。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# トレーニング前のベースモデルを試してみましょう\n",
+    "prompt = \"プログラミングについての俳句を書いてください\"\n",
+    "\n",
+    "# テンプレートでフォーマット\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "# 応答を生成\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
+    "print(\"トレーニング前:\")\n",
+    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## データセットの準備\n",
+    "\n",
+    "例としてデータセットをロードし、トレーニング用にフォーマットします。データセットは、各入力がプロンプトで、出力がモデルの期待される応答である入力-出力ペアで構成されている必要があります。\n",
+    "\n",
+    "**TRLは、モデルのチャットテンプレートに従って入力メッセージをフォーマットします。** これらは、`role`と`content`キーを持つ辞書のリストとして表現される必要があります。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 例としてデータセットをロード\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: パスと名前のパラメータを使用してデータセットと設定を定義\n",
+    "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: 🦁 データセットがTRLでチャットテンプレートに変換できない形式の場合、処理が必要です。 [モジュール](../chat_templates.md)を参照してください。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SFTTrainerの設定\n",
+    "\n",
+    "`SFTTrainer`は、トレーニングプロセスを制御するさまざまなパラメータで設定されます。これには、トレーニングステップの数、バッチサイズ、学習率、および評価戦略が含まれます。これらのパラメータを特定の要件と計算リソースに応じて調整します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SFTTrainerを設定\n",
+    "sft_config = SFTConfig(\n",
+    "    output_dir=\"./sft_output\",  # トレーニング結果の出力ディレクトリ\n",
+    "    max_steps=1000,  # データセットのサイズとトレーニングの所要時間に応じて調整\n",
+    "    per_device_train_batch_size=4,  # GPUのメモリ容量に応じて調整\n",
+    "    learning_rate=5e-5,  # 微調整の一般的な開始点\n",
+    "    logging_steps=10,  # トレーニングメトリックのログ記録頻度\n",
+    "    save_steps=100,  # モデルのチェックポイント保存頻度\n",
+    "    evaluation_strategy=\"steps\",  # 定期的な間隔でモデルを評価\n",
+    "    eval_steps=50,  # 評価の頻度\n",
+    "    use_mps_device=(\n",
+    "        True if device == \"mps\" else False\n",
+    "    ),  # 混合精度トレーニングにMPSを使用\n",
+    "    hub_model_id=finetune_name,  # モデルに一意の名前を付ける\n",
+    ")\n",
+    "\n",
+    "# SFTTrainerを初期化\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=sft_config,\n",
+    "    train_dataset=ds[\"train\"],\n",
+    "    tokenizer=tokenizer,\n",
+    "    eval_dataset=ds[\"test\"],\n",
+    ")\n",
+    "\n",
+    "# TODO: 🦁 🐕 選択したデータセットに合わせてSFTTrainerのパラメータを調整します。例えば、`bigcode/the-stack-smol`データセットを使用する場合、`content`列を選択する必要があります。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## モデルのトレーニング\n",
+    "\n",
+    "トレーナーが設定されたので、モデルのトレーニングを進めることができます。トレーニングプロセスは、データセットを反復し、損失を計算し、この損失を最小化するためにモデルのパラメータを更新することを含みます。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# モデルをトレーニング\n",
+    "trainer.train()\n",
+    "\n",
+    "# モデルを保存\n",
+    "trainer.save_model(f\"./{finetune_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(tags=finetune_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>追加の演習: 微調整されたモデルでの生成</h2>\n",
+    "    <p>🐕 微調整されたモデルを使用して、ベースモデルの例と同じように応答を生成します。</p>\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 同じメッセージで微調整されたモデルをテスト\n",
+    "\n",
+    "# トレーニング前のベースモデルを試してみましょう\n",
+    "prompt = \"プログラミングについての俳句を書いてください\"\n",
+    "\n",
+    "# テンプレートでフォーマット\n",
+    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n",
+    "\n",
+    "# 応答を生成\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "\n",
+    "# TODO: 微調整されたモデルを使用して、ベースモデルの例と同じように応答を生成します。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完了しました！\n",
+    "\n",
+    "このノートブックでは、`SFTTrainer`を使用して`HuggingFaceTB/SmolLM2-135M`モデルを微調整するためのステップバイステップガイドを提供しました。これらの手順に従うことで、特定のタスクをより効果的に実行するためにモデルを適応させることができます。このコースを続けて作業したい場合、以下の提案を試してみてください：\n",
+    "\n",
+    "- より高い難易度レベルでこのノートブックを試してみてください。\n",
+    "- 同僚のPRをレビューしてください。\n",
+    "- IssueやPRを通じてコースの資料を改善してください。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ja/1_instruction_tuning/supervised_fine_tuning.md b/ja/1_instruction_tuning/supervised_fine_tuning.md
new file mode 100644
index 00000000..e6e51ad9
--- /dev/null
+++ b/ja/1_instruction_tuning/supervised_fine_tuning.md
@@ -0,0 +1,41 @@
+# 教師あり微調整
+
+教師あり微調整（SFT）は、事前トレーニングされた言語モデルを特定のタスクやドメインに適応させるための重要なプロセスです。事前トレーニングされたモデルは一般的な能力を持っていますが、特定のユースケースで優れたパフォーマンスを発揮するためにはカスタマイズが必要です。SFTは、慎重に選ばれた人間によって検証された例を含むデータセットを使用してモデルをトレーニングすることで、このギャップを埋めます。
+
+## 教師あり微調整の理解
+
+教師あり微調整の核心は、事前トレーニングされたモデルに特定のタスクを実行する方法を教えることです。これは、入力と出力の例を多数提示し、モデルが特定のユースケースのパターンを学習できるようにすることを含みます。
+
+SFTは、事前トレーニング中に獲得した基本的な知識を活用しながら、モデルの動作を特定のニーズに合わせて調整するため、効果的です。
+
+## 教師あり微調整を使用するタイミング
+
+SFTを使用するかどうかの決定は、現在のモデルの能力と特定の要件とのギャップに依存します。SFTは、モデルの出力を正確に制御する必要がある場合や、専門的なドメインで作業する場合に特に価値があります。
+
+例えば、カスタマーサービスアプリケーションを開発している場合、モデルが一貫して企業のガイドラインに従い、技術的な問い合わせを標準化された方法で処理することを望むかもしれません。同様に、医療や法律のアプリケーションでは、正確さと特定のドメインの用語に従うことが重要です。これらの場合、SFTはモデルの応答を専門的な基準とドメインの専門知識に合わせるのに役立ちます。
+
+## 教師あり微調整（SFT）のプロセス
+
+教師あり微調整のプロセスは、特定のタスクのデータセットでモデルの重みをトレーニングすることを含みます。
+
+まず、ターゲットタスクを表すデータセットを準備または選択する必要があります。このデータセットには、モデルが直面するさまざまなシナリオをカバーする多様な例が含まれている必要があります。これらのデータの品質は重要です。各例は、モデルが生成することを望む出力の種類を示している必要があります。次に、微調整のフェーズがあり、ここでは`transformers`やHugging Faceの`trl`などのフレームワークを使用して、データセットでモデルをトレーニングします。
+
+プロセス全体を通じて、継続的な評価が重要です。モデルが望ましい動作を学習していることを確認するために、検証セットでのパフォーマンスを監視する必要があります。これにより、モデルが一般的な能力を失うことなく、特定のタスクに適応していることを確認できます。[モジュール4](../4_evaluation)では、モデルの評価方法について詳しく説明します。
+
+## 教師あり微調整の役割
+
+SFTは、言語モデルを人間の好みに合わせる上で重要な役割を果たします。人間のフィードバックを使用した強化学習（RLHF）や直接選好最適化（DPO）などの技術は、SFTを使用してタスクの基本的な理解を形成し、その後、モデルの応答を望ましい結果にさらに合わせます。事前トレーニングされたモデルは、一般的な言語能力に優れているにもかかわらず、必ずしも人間の好みに一致する出力を生成するわけではありません。SFTは、特定のドメインのデータとガイダンスを導入することで、このギャップを埋め、モデルが人間の期待により密接に一致する応答を生成する能力を向上させます。
+
+## トランスフォーマーの強化学習（TRL）を使用した教師あり微調整
+
+教師あり微調整のための主要なソフトウェアパッケージは、トランスフォーマーの強化学習（TRL）です。TRLは、強化学習（RL）を使用してトランスフォーマーモデルをトレーニングするためのツールセットです。
+
+Hugging FaceのTransformersライブラリに基づいており、ユーザーが事前トレーニングされた言語モデルを直接ロードできるようにし、ほとんどのデコーダーおよびエンコーダーデコーダーアーキテクチャと互換性があります。このライブラリは、教師あり微調整（SFT）、報酬モデリング（RM）、近接ポリシー最適化（PPO）、および直接選好最適化（DPO）など、言語モデリングで使用される主要なRLプロセスを容易にします。このリポジトリ全体で、さまざまなモジュールでTRLを使用します。
+
+# 次のステップ
+
+次のチュートリアルを試して、TRLを使用したSFTの実践的な経験を積んでください：
+
+⏭️ [チャットテンプレートのチュートリアル](./notebooks/chat_templates_example.ipynb)
+
+⏭️ [教師あり微調整のチュートリアル](./notebooks/supervised_fine_tuning_tutorial.ipynb)
diff --git a/ja/2_preference_alignment/README.md b/ja/2_preference_alignment/README.md
new file mode 100644
index 00000000..33f0643c
--- /dev/null
+++ b/ja/2_preference_alignment/README.md
@@ -0,0 +1,39 @@
+# 選好の整合
+
+このモジュールでは、言語モデルを人間の選好に合わせるための技術について説明します。教師あり微調整（SFT）がモデルにタスクを学習させるのに役立つ一方で、選好の整合は出力が人間の期待や価値観に一致するようにします。
+
+## 概要
+
+選好の整合の典型的な方法には、複数のステージが含まれます：
+1. 教師あり微調整（SFT）でモデルを特定のドメインに適応させる。
+2. 選好の整合（RLHFやDPOなど）で応答の質を向上させる。
+
+ORPOのような代替アプローチは、指示調整と選好の整合を単一のプロセスに統合します。ここでは、DPOとORPOのアルゴリズムに焦点を当てます。
+
+さまざまな整合技術について詳しく知りたい場合は、[Argillaのブログ](https://argilla.io/blog/mantisnlp-rlhf-part-8)を参照してください。
+
+### 1️⃣ 直接選好最適化（DPO）
+
+直接選好最適化（DPO）は、選好データを使用してモデルを直接最適化することで、選好の整合を簡素化します。このアプローチは、別個の報酬モデルや複雑な強化学習を必要とせず、従来のRLHFよりも安定して効率的です。詳細については、[直接選好最適化（DPO）のドキュメント](./dpo.md)を参照してください。
+
+### 2️⃣ 選好確率比最適化（ORPO）
+
+ORPOは、指示調整と選好の整合を単一のプロセスに統合する新しいアプローチを導入します。これは、負の対数尤度損失とトークンレベルのオッズ比項を組み合わせて標準的な言語モデリングの目的を修正します。このアプローチは、単一のトレーニングステージ、参照モデル不要のアーキテクチャ、および計算効率の向上を提供します。ORPOは、さまざまなベンチマークで印象的な結果を示しており、従来の方法と比較してAlpacaEvalで優れたパフォーマンスを示しています。詳細については、[選好確率比最適化（ORPO）のドキュメント](./orpo.md)を参照してください。
+
+## 実習ノートブック
+
+| タイトル | 説明 | 実習内容 | リンク | Colab |
+|-------|-------------|----------|------|-------|
+| DPOトレーニング | 直接選好最適化を使用してモデルを��レーニングする方法を学ぶ | 🐢 AnthropicのHH-RLHFデータセットを使用してモデルをトレーニングする<br>🐕 独自の選好データセットを使用する<br>🦁 さまざまな選好データセットとモデルサイズで実験する | [ノートブック](./notebooks/dpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| ORPOトレーニング | 選好確率比最適化を使用してモデルをトレーニングする方法を学ぶ | 🐢 指示と選好データを使用してモデルをトレーニングする<br>🐕 損失の重みを変えて実験する<br>🦁 ORPOとDPOの結果を比較する | [ノートブック](./notebooks/orpo_finetuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## リソース
+
+- [TRLのドキュメント](https://huggingface.co/docs/trl/index) - DPOを含むさまざまな整合技術を実装するためのTransformers Reinforcement Learning（TRL）ライブラリのドキュメント。
+- [DPO論文](https://arxiv.org/abs/2305.18290) - 人間のフィードバックを用いた強化学習の代替として、選好データを使用して言語モデルを直接最適化するシンプルなアプローチを紹介する論文。
+- [ORPO論文](https://arxiv.org/abs/2403.07691) - 指示調整と選好の整合を単一のトレーニングステージに統合する新しいアプローチを紹介する論文。
+- [ArgillaのRLHFガイド](https://argilla.io/blog/mantisnlp-rlhf-part-8/) - RLHF、DPOなどのさまざまな整合技術とその実践的な実装について説明するガイド。
+- [DPOに関するブログ記事](https://huggingface.co/blog/dpo-trl) - TRLライブラリを使用してDPOを実装する方法についての実践ガイド。コード例とベストプラクティスが含まれています。
+- [TRLのDPOスクリプト例](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py) - TRLライブラリを使用してDPOトレーニングを実装する方法を示す完全なスクリプト例。
+- [TRLのORPOスクリプト例](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) - TRLライブラリを使用してORPOトレーニングを実装するためのリファレンス実装。詳細な設定オプションが含まれています。
+- [Hugging Faceの整合ハンドブック](https://github.com/huggingface/alignment-handbook) - SFT、DPO、RLHFなどのさまざまな技術を使用して言語モデルを整合させるためのガイドとコード。
diff --git a/ja/2_preference_alignment/dpo.md b/ja/2_preference_alignment/dpo.md
new file mode 100644
index 00000000..6d354d35
--- /dev/null
+++ b/ja/2_preference_alignment/dpo.md
@@ -0,0 +1,73 @@
+**直接選好最適化（DPO）**
+
+直接選好最適化（DPO）は、言語モデルを人間の好みに合わせるための簡素化されたアプローチを提供します。従来のRLHF（人間のフィードバックを用いた強化学習）メソッドとは異なり、DPOは別個の報酬モデルや複雑な強化学習アルゴリズムを必要とせず、選好データを使用してモデルを直接最適化します。
+
+## DPOの理解
+
+DPOは、選好の整合を人間の選好データに基づく分類問題として再定義します。従来のRLHFアプローチでは、別個の報酬モデルをトレーニングし、PPO（近接ポリシー最適化）などの複雑なアルゴリズムを使用してモデルの出力を整合させる必要があります。DPOは、好ましい出力と好ましくない出力に基づいてモデルのポリシーを直接最適化する損失関数を定義することで、このプロセスを簡素化します。
+
+このアプローチは実際に非常に効果的であり、Llamaなどのモデルのトレーニングに使用されています。別個の報酬モデルや強化学習のステージを必要としないため、DPOは選好の整合をよりアクセスしやすく、安定したものにします。
+
+## DPOの仕組み
+
+DPOのプロセスには、ターゲットドメインにモデルを適応させるための教師あり微調整（SFT）が必要です。これにより、標準的な指示追従データセットでトレーニングすることで、選好学習の基盤が形成されます。モデルは基本的なタスクを完了しながら、一般的な能力を維持することを学びます。
+
+次に、選好学習が行われ、モデルは好ましい出力と好ましくない出力のペアでトレーニングされます。選好ペアは、モデルがどの応答が人間の価値観や期待に最も一致するかを理解するのに役立ちます。
+
+DPOの中心的な革新は、その直接最適化アプローチにあります。別個の報酬モデルをトレーニングする代わりに、DPOはバイナリクロスエントロピー損失を使用して、選好データに基づいてモデルの重みを直接更新します。この簡素化されたプロセスにより、トレーニングがより安定し、効率的になり、従来のRLHFメソッドと同等またはそれ以上の結果が得られます。
+
+## DPOのデータセット
+
+DPOのデータセットは、通常、選好または非選好として注釈された応答ペアを含むように作成されます。これは手動で行うか、自動フィルタリング技術を使用して行うことができます。以下は、単一ターンのDPO選好データセットの構造の例です：
+
+| プロンプト | 選好 | 非選好 |
+|--------|---------|-----------|
+| ...    | ...     | ...       |
+| ...    | ...     | ...       |
+| ...    | ...     | ...       |
+
+`Prompt`列には、`選好`および`非選好`の応答を生成するために使用されたプロンプトが含まれています。`選好`および`非選好`列には、それぞれ好ましい応答と好ましくない応答が含まれています。この構造にはバリエーションがあり、例えば、`system_prompt`や`Input`列に参照資料を含めることができます。`選好`および`非選好`の値は、単一ターンの会話の場合は文字列として、または会話リストとして表現されることがあります。
+
+Hugging FaceでDPOデータセットのコレクションを[こちら](https://huggingface.co/collections/argilla/preference-datasets-for-dpo-656f0ce6a00ad2dc33069478)で見つけることができます。
+
+## TRLを使用した実装
+
+Transformers Reinforcement Learning（TRL）ライブラリは、DPOの実装を容易にします。`DPOConfig`および`DPOTrainer`クラスは、`transformers`スタイルのAPIに従います。
+
+以下は、DPOトレーニングを設定する基本的な例です：
+
+```python
+from trl import DPOConfig, DPOTrainer
+
+# 引数を定義
+training_args = DPOConfig(
+    ...
+)
+
+# トレーナーを初期化
+trainer = DPOTrainer(
+    model,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+    ...
+)
+
+# モデルをトレーニング
+trainer.train()
+```
+
+DPOConfigおよびDPOTrainerクラスの使用方法の詳細については、[DPOチュートリアル](./notebooks/dpo_finetuning_example.ipynb)を参照してください。
+
+## ベストプラクティス
+
+データの品質は、DPOの成功した実装にとって重要です。選好データセットには、望ましい行動のさまざまな側面をカバーする多様な例が含まれている必要があります。明確な注釈ガイドラインは、選好および非選好の応答の一貫したラベル付けを保証します。選好データセットの品質を向上させることで、モデルのパフォーマンスを向上させることができます。例えば、より大きなデータセットをフィルタリングして、高品質の例やユースケースに関連する例のみを含めることができます。
+
+トレーニング中は、損失の収束を慎重に監視し、保持データでパフォーマンスを検証します。選好学習とモデルの一般的な能力の維持をバランスさせるために、ベータパラメータを調整する必要がある場合があります。多様な例での定期的な評価は、モデルが望ましい選好を学習しながら過剰適合しないことを保証するのに役立ちます。
+
+モデルの出力を基準モデルと比較して、選好の整合性の向上を確認します。さまざまなプロンプト、特にエッジケースを使用してモデルをテストすることで、さまざまなシナリオでの選好学習の堅牢性を保証します。
+
+## 次のステップ
+
+⏩ DPOの実践的な経験を得るために、[DPOチュートリアル](./notebooks/dpo_finetuning_example.ipynb)を試してみてください。この実践ガイドでは、データの準備からトレーニングおよび評価まで、選好整合の実装方法を説明します。
+
+⏭️ チュートリアルを完了した後、別の選好整合技術について学ぶために[ORPOページ](./orpo.md)を探索してください。
diff --git a/ja/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/ja/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
new file mode 100644
index 00000000..38f63fde
--- /dev/null
+++ b/ja/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DPO微調整の例\n",
+    "\n",
+    "このノートブックでは、`trl`ライブラリを使用してDPO（直接選好最適化）を実行する方法を示します。DPOは、モデルの出力を人間の選好に合わせるためのシンプルで効果的な方法です。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. 環境の設定\n",
+    "\n",
+    "まず、必要なライブラリをインストールし、Hugging Faceにログインします。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件のインストール\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Hugging Faceへの認証\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# 便利のため、Hugging Faceのトークンを.envファイルのHF_TOKENとして環境変数に設定できます"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. モデルとデータセットのロード\n",
+    "\n",
+    "次に、事前学習済みモデルとデータセットをロードします。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# モデルとトークナイザーをロード\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "\n",
+    "# データセットをロード\n",
+    "dataset = load_dataset(\"Anthropic/hh-rlhf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. DPOトレーナーの設定\n",
+    "\n",
+    "DPOトレーナーを設定し、トレーニングを開始します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trl import DPOConfig, DPOTrainer\n",
+    "\n",
+    "# DPOの設定\n",
+    "dpo_config = DPOConfig(\n",
+    "    model_name_or_path=model_name,\n",
+    "    learning_rate=5e-5,\n",
+    "    per_device_train_batch_size=4,\n",
+    "    num_train_epochs=3,\n",
+    "    logging_steps=10,\n",
+    "    save_steps=100,\n",
+    "    output_dir=\"./dpo_output\"\n",
+    ")\n",
+    "\n",
+    "# DPOトレーナーを初期化\n",
+    "trainer = DPOTrainer(\n",
+    "    model=model,\n",
+    "    args=dpo_config,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    eval_dataset=dataset[\"test\"],\n",
+    "    tokenizer=tokenizer\n",
+    ")\n",
+    "\n",
+    "# モデルをトレーニング\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. 微調整されたモデルの保存\n",
+    "\n",
+    "トレーニングが完了したら、微調整されたモデルを保存します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 微調整されたモデルを保存\n",
+    "trainer.save_model(\"./dpo_finetuned_model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. 微調整されたモデルの評価\n",
+    "\n",
+    "最後に、微調整されたモデルを評価します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 評価の実行\n",
+    "results = trainer.evaluate()\n",
+    "print(results)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ja/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/ja/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
new file mode 100644
index 00000000..e4fc09ca
--- /dev/null
+++ b/ja/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb
@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ORPO微調整の例\n",
+    "\n",
+    "このノートブックでは、`trl`ライブラリを使用してORPO（選好確率比最適化）を実行する方法を示します。ORPOは、モデルの出力を人間の選好に合わせるためのシンプルで効果的な方法です。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. 環境の設定\n",
+    "\n",
+    "まず、必要なライブラリをインストールし、Hugging Faceにログインします。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件のインストール\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Hugging Faceへの認証\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# 便利のため、Hugging Faceのトークンを.envファイルのHF_TOKENとして環境変数に設定できます"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. モデルとデータセットのロード\n",
+    "\n",
+    "次に、事前学習済みモデルとデータセットをロードします。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# モデルとトークナイザーをロード\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "\n",
+    "# データセットをロード\n",
+    "dataset = load_dataset(\"Anthropic/hh-rlhf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. ORPOトレーナーの設定\n",
+    "\n",
+    "ORPOトレーナーを設定し、トレーニングを開始します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trl import ORPOConfig, ORPOTrainer\n",
+    "\n",
+    "# ORPOの設定\n",
+    "orpo_config = ORPOConfig(\n",
+    "    model_name_or_path=model_name,\n",
+    "    learning_rate=5e-5,\n",
+    "    per_device_train_batch_size=4,\n",
+    "    num_train_epochs=3,\n",
+    "    logging_steps=10,\n",
+    "    save_steps=100,\n",
+    "    output_dir=\"./orpo_output\"\n",
+    ")\n",
+    "\n",
+    "# ORPOトレーナーを初期化\n",
+    "trainer = ORPOTrainer(\n",
+    "    model=model,\n",
+    "    args=orpo_config,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    eval_dataset=dataset[\"test\"],\n",
+    "    tokenizer=tokenizer\n",
+    ")\n",
+    "\n",
+    "# モデルをトレーニング\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. 微調整されたモデルの保存\n",
+    "\n",
+    "トレーニングが完了したら、微調整されたモデルを保存します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 微調整されたモデルを保存\n",
+    "trainer.save_model(\"./orpo_finetuned_model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. 微調整されたモデルの評価\n",
+    "\n",
+    "最後に、微調整されたモデルを評価します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 評価の実行\n",
+    "results = trainer.evaluate()\n",
+    "print(results)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ja/2_preference_alignment/orpo.md b/ja/2_preference_alignment/orpo.md
new file mode 100644
index 00000000..ac3a4af6
--- /dev/null
+++ b/ja/2_preference_alignment/orpo.md
@@ -0,0 +1,83 @@
+**選好確率比最適化（ORPO）**
+
+選好確率比最適化（ORPO）は、微調整と選好整合を単一の統合プロセスで組み合わせる新しいアプローチです。この統合アプローチは、従来のRLHFやDPOなどの方法と比較して、効率とパフォーマンスの面で利点を提供します。
+
+## ORPOの理解
+
+DPOのような選好整合方法は、通常、微調整と選好整合の2つの別々のステップを含みます。微調整はモデルを特定のドメインや形式に適応させ、選好整合は人間の選好に合わせて出力を調整します。SFT（教師あり微調整）は、モデルをターゲットドメインに適応させるのに効果的ですが、望ましい応答と望ましくない応答の両方の確率を増加させる可能性があります。ORPOは、以下の比較図に示すように、これらのステップを単一のプロセスに統合することで、この制限に対処します。
+
+![モデル整合技術の比較](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-alignments.png)
+*モデル整合技術の比較*
+
+## ORPOの仕組み
+
+ORPOのトレーニングプロセスは、DPOと同様に、入力プロンプトと2つの応答（1つは選好、もう1つは非選好）を含む選好データセットを使用します。他の整合方法とは異なり、ORPOは選好整合を教師あり微調整プロセスに直接統合します。この統合アプローチにより、参照モデルが不要になり、計算効率とメモリ効率が向上し、FLOP数が減少します。
+
+ORPOは、次の2つの主要なコンポーネントを組み合わせて新しい目的を作成します：
+
+1. **SFT損失**：負の対数尤度損失を使用して、参照トークンの生成確率を最大化します。これにより、モデルの一般的な言語能力が維持されます。
+2. **オッズ比損失**：望ましくない応答をペナルティし、選好される応答を報酬する新しいコンポーネントです。この損失関数は、トークンレベルで選好される応答と非選好の応答を効果的に対比するためにオッズ比を使用します。
+
+これらのコンポーネントを組み合わせることで、モデルは特定のドメインに対して望ましい生成を適応させながら、非選好の応答を積極的に抑制します。オッズ比メカニズムは、選好された応答と拒否された応答の間のモデルの選好を測定および最適化する自然な方法を提供します。数学的な詳細については、[ORPO論文](https://arxiv.org/abs/2403.07691)を参照してください。実装の観点からORPOについて学びたい場合は、[TRLライブラリ](https://github.com/huggingface/trl/blob/b02189aaa538f3a95f6abb0ab46c0a971bfde57e/trl/trainer/orpo_trainer.py#L660)でORPO損失がどのように計算されるかを確認してください。
+
+## パフォーマンスと結果
+
+ORPOは、さまざまなベンチマークで印象的な結果を示しています。MT-Benchでは、さまざまなカテゴリで競争力のあるスコアを達成しています：
+
+![MT-Benchの結果](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-mtbench.png)
+*MT-Benchのカテゴリ別結果（Mistral-ORPOモデル）*
+
+他の整合方法と比較すると、ORPOはAlpacaEval 2.0で優れたパフォーマンスを示しています：
+
+![AlpacaEvalの結果](https://argilla.io/images/blog/mantisnlp-rlhf/part-8-winrate.png)
+*さまざまな整合方法におけるAlpacaEval 2.0スコア*
+
+SFT+DPOと比較して、ORPOは参照モデルが不要であり、バッチごとのフォワードパスの数を半減させることで計算要件を削減します。さらに、トレーニングプロセスは、さまざまなモデルサイズとデータセットでより安定しており、調整するハイパーパラメータが少なくて済みます。パフォーマンスに関しては、ORPOはより大きなモデルと同等のパフォーマンスを示しながら、人間の選好に対する整合性が向上しています。
+
+## 実装
+
+ORPOの成功した実装は、高品質の選好データに大きく依存します。トレーニングデータは明確な注釈ガイドラインに従い、さまざまなシナリオで好ましい応答と拒否された応答のバランスの取れた表現を提供する必要があります。
+
+### TRLを使用した実装
+
+ORPOは、Transformers Reinforcement Learning（TRL）ライブラリを使用して実装できます。以下は基本的な例です：
+
+```python
+from trl import ORPOConfig, ORPOTrainer
+
+# ORPOトレーニングの設定
+orpo_config = ORPOConfig(
+    learning_rate=1e-5,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    max_steps=1000,
+    orpo_alpha=1.0,  # 選好最適化の強度を制御
+    orpo_beta=0.1,   # オッズ比計算の温度パラメータ
+)
+
+# トレーナーを初期化
+trainer = ORPOTrainer(
+    model=model,
+    args=orpo_config,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+)
+
+# トレーニングを開始
+trainer.train()
+```
+
+考慮すべき主要なパラメータ：
+- `orpo_alpha`：選好最適化の強度を制御
+- `orpo_beta`：オッズ比計算の温度パラメータ
+- `learning_rate`：忘却のカタストロフィーを避けるために比較的小さく設定
+- `gradient_accumulation_steps`：トレーニングの安定性を向上
+
+## 次のステップ
+
+⏩ この統合された選好整合アプローチを実装するための[ORPOチュートリアル](./notebooks/orpo_tutorial.ipynb)を試してみてください。
+
+## リソース
+- [ORPO論文](https://arxiv.org/abs/2403.07691)
+- [TRLドキュメント](https://huggingface.co/docs/trl/index)
+- [ArgillaのRLHFガイド](https://argilla.io/blog/mantisnlp-rlhf-part-8/)

From 34f73f08024b8105df21528e9ec01c840ff4d407 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Wed, 15 Jan 2025 16:58:17 +0100
Subject: [PATCH 39/63] lose agent definition

---
 8_agents/notebooks/agents.ipynb | 36 ++++++++++++++-------------------
 8_agents/retrieval_agents.md    |  4 ++--
 2 files changed, 17 insertions(+), 23 deletions(-)

diff --git a/8_agents/notebooks/agents.ipynb b/8_agents/notebooks/agents.ipynb
index 63dfc0cd..02d278c6 100644
--- a/8_agents/notebooks/agents.ipynb
+++ b/8_agents/notebooks/agents.ipynb
@@ -48,10 +48,7 @@
                 "from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel\n",
                 "\n",
                 "# Initialize the agent\n",
-                "agent = CodeAgent(\n",
-                "    tools=[DuckDuckGoSearchTool()],\n",
-                "    model=HfApiModel()\n",
-                ")\n",
+                "agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())\n",
                 "\n",
                 "# Test the agent\n",
                 "response = agent.run(\"What's the difference between a list and a tuple in Python?\")\n",
@@ -87,38 +84,37 @@
                 "from smolagents import CodeAgent, tool\n",
                 "from typing import Union\n",
                 "\n",
+                "\n",
                 "@tool\n",
                 "def calculate(operation: str, numbers: object) -> float:\n",
                 "    \"\"\"Performs basic mathematical operations on a list of numbers.\n",
-                "    \n",
+                "\n",
                 "    Args:\n",
                 "        operation: One of 'sum', 'average', 'multiply', 'min', 'max'\n",
                 "        numbers: List of numbers to operate on\n",
-                "    \n",
+                "\n",
                 "    Returns:\n",
                 "        float: Result of the operation\n",
                 "    \"\"\"\n",
-                "    if operation == 'sum':\n",
+                "    if operation == \"sum\":\n",
                 "        return sum(numbers)\n",
-                "    elif operation == 'average':\n",
+                "    elif operation == \"average\":\n",
                 "        return sum(numbers) / len(numbers)\n",
-                "    elif operation == 'multiply':\n",
+                "    elif operation == \"multiply\":\n",
                 "        result = 1\n",
                 "        for n in numbers:\n",
                 "            result *= n\n",
                 "        return result\n",
-                "    elif operation == 'min':\n",
+                "    elif operation == \"min\":\n",
                 "        return min(numbers)\n",
-                "    elif operation == 'max':\n",
+                "    elif operation == \"max\":\n",
                 "        return max(numbers)\n",
                 "    else:\n",
                 "        raise ValueError(f\"Unknown operation: {operation}\")\n",
                 "\n",
+                "\n",
                 "# Create agent with custom tool\n",
-                "math_agent = CodeAgent(\n",
-                "    tools=[calculate],\n",
-                "    model=HfApiModel()\n",
-                ")\n",
+                "math_agent = CodeAgent(tools=[calculate], model=HfApiModel())\n",
                 "\n",
                 "# Test the agent\n",
                 "response = math_agent.run(\"What is the average of 10, 15, 20, 25, and 30?\")\n",
@@ -150,22 +146,20 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "from smolagents import Agent\n",
+                "from smolagents import CodeAgent\n",
                 "from smolagents.tools import DuckDuckGoSearch\n",
                 "\n",
                 "# Initialize the agent with memory\n",
-                "research_agent = Agent(\n",
-                "    ... # TODO: Define the agent\n",
-                ")\n",
+                "research_agent = CodeAgent(...)  # TODO: Define the agent\n",
                 "\n",
                 "# Test with a multi-turn conversation\n",
                 "questions = [\n",
                 "    \"What are the main types of machine learning?\",\n",
                 "    \"Can you explain supervised learning in more detail?\",\n",
-                "    \"What are some popular algorithms for this type?\"\n",
+                "    \"What are some popular algorithms for this type?\",\n",
                 "]\n",
                 "\n",
-                "# TODO: Test the agent  "
+                "# TODO: Test the agent"
             ]
         },
         {
diff --git a/8_agents/retrieval_agents.md b/8_agents/retrieval_agents.md
index fcd70d28..1b5e4ed7 100644
--- a/8_agents/retrieval_agents.md
+++ b/8_agents/retrieval_agents.md
@@ -9,14 +9,14 @@ Traditional RAG has key limitations - it only performs a single retrieval step a
 Let's start by building a simple agent that can search the web using DuckDuckGo. This agent will be able to answer questions by retrieving relevant information and synthesizing responses.
 
 ```python
-from smolagents import Agent
+from smolagents import CodeAgent
 from smolagents.tools import DuckDuckGoSearch
 
 # Initialize the search tool
 search_tool = DuckDuckGoSearch()
 
 # Create an agent with memory
-agent = Agent(
+agent = CodeAgent(
     name="research_assistant",
     description="I help find and synthesize information from the web",
     tools=[search_tool]

From 097454d379f1b0a907fbb00a8ab37e71704cb55d Mon Sep 17 00:00:00 2001
From: Sarthak Bhardwaj <7sarthakbhardwaj@gmail.com>
Date: Wed, 15 Jan 2025 22:58:17 +0530
Subject: [PATCH 40/63] Add import List

---
 4_evaluation/project/generate_dataset.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/4_evaluation/project/generate_dataset.py b/4_evaluation/project/generate_dataset.py
index cb531d3f..e0dbd6ed 100644
--- a/4_evaluation/project/generate_dataset.py
+++ b/4_evaluation/project/generate_dataset.py
@@ -2,6 +2,7 @@
 import os
 from pydantic import BaseModel, Field
 from datasets import Dataset
+from typing import List
 
 from distilabel.llms import InferenceEndpointsLLM
 from distilabel.pipeline import Pipeline

From 08aba6be2fcf89e2848d2d9049c7a9c2e0ba68f6 Mon Sep 17 00:00:00 2001
From: Batur <52473505+baturyilmaz@users.noreply.github.com>
Date: Wed, 15 Jan 2025 22:19:53 +0300
Subject: [PATCH 41/63] fix: correct CodeAgent usage in retrieval_agents.md

- Fix incorrect imports
- Fix wrong tool class (DuckDuckGoSearch -> DuckDuckGoSearchTool)
- Add missing HfApiModel requirement
- Remove incorrect memory comment
- Remove ManagedAgent parameters
---
 8_agents/retrieval_agents.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/8_agents/retrieval_agents.md b/8_agents/retrieval_agents.md
index 1b5e4ed7..97c6ed0b 100644
--- a/8_agents/retrieval_agents.md
+++ b/8_agents/retrieval_agents.md
@@ -9,16 +9,16 @@ Traditional RAG has key limitations - it only performs a single retrieval step a
 Let's start by building a simple agent that can search the web using DuckDuckGo. This agent will be able to answer questions by retrieving relevant information and synthesizing responses.
 
 ```python
-from smolagents import CodeAgent
-from smolagents.tools import DuckDuckGoSearch
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
 
 # Initialize the search tool
-search_tool = DuckDuckGoSearch()
+search_tool = DuckDuckGoSearchTool()
+
+# Initialize the model
+model = HfApiModel()
 
-# Create an agent with memory
 agent = CodeAgent(
-    name="research_assistant",
-    description="I help find and synthesize information from the web",
+    model = model,
     tools=[search_tool]
 )
 

From f5ac32156850ed4aa9b556ccae5ef3edbcb67742 Mon Sep 17 00:00:00 2001
From: Ikko Eltociear Ashimine <eltociear@gmail.com>
Date: Fri, 17 Jan 2025 00:26:05 +0900
Subject: [PATCH 42/63] [TRANSLATION] Add 3_parameter_efficient_finetuning and
 4_evaluation EN-JA translation

Add Japanese translated documents.
---
 ja/3_parameter_efficient_finetuning/README.md |  39 ++
 .../lora_adapters.md                          | 124 +++++
 .../notebooks/finetune_sft_peft.ipynb         | 516 ++++++++++++++++++
 .../notebooks/load_lora_adapter.ipynb         | 161 ++++++
 .../prompt_tuning.md                          |  74 +++
 ja/4_evaluation/README.md                     |  39 ++
 ja/4_evaluation/automatic_benchmarks.md       | 131 +++++
 ja/4_evaluation/custom_evaluation.md          | 132 +++++
 ...hteval_evaluate_and_analyse_your_LLM.ipynb | 210 +++++++
 ja/4_evaluation/project/README.md             |  83 +++
 ja/4_evaluation/project/annotate_dataset.py   | 129 +++++
 ja/4_evaluation/project/create_dataset.py     |  72 +++
 ja/4_evaluation/project/evaluation_task.py    |  87 +++
 ja/4_evaluation/project/generate_dataset.py   | 168 ++++++
 ja/README.md                                  |  90 +++
 15 files changed, 2055 insertions(+)
 create mode 100644 ja/3_parameter_efficient_finetuning/README.md
 create mode 100644 ja/3_parameter_efficient_finetuning/lora_adapters.md
 create mode 100644 ja/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
 create mode 100644 ja/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
 create mode 100644 ja/3_parameter_efficient_finetuning/prompt_tuning.md
 create mode 100644 ja/4_evaluation/README.md
 create mode 100644 ja/4_evaluation/automatic_benchmarks.md
 create mode 100644 ja/4_evaluation/custom_evaluation.md
 create mode 100644 ja/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
 create mode 100644 ja/4_evaluation/project/README.md
 create mode 100644 ja/4_evaluation/project/annotate_dataset.py
 create mode 100644 ja/4_evaluation/project/create_dataset.py
 create mode 100644 ja/4_evaluation/project/evaluation_task.py
 create mode 100644 ja/4_evaluation/project/generate_dataset.py
 create mode 100644 ja/README.md

diff --git a/ja/3_parameter_efficient_finetuning/README.md b/ja/3_parameter_efficient_finetuning/README.md
new file mode 100644
index 00000000..f375fc32
--- /dev/null
+++ b/ja/3_parameter_efficient_finetuning/README.md
@@ -0,0 +1,39 @@
+# パラメータ効率の良い微調整 (PEFT)
+
+言語モデルが大きくなるにつれて、従来の微調整はますます困難になります。1.7Bパラメータのモデルの完全な微調整には、かなりのGPUメモリが必要であり、別々のモデルコピーを保存することが高価であり、モデルの元の能力を破壊的に忘れるリスクがあります。パラメータ効率の良い微調整（PEFT）メソッドは、モデルパラメータの小さなサブセットのみを変更し、ほとんどのモデルを固定したままにすることで、これらの課題に対処します。
+
+従来の微調整は、トレーニング中にすべてのモデルパラメータを更新しますが、これは大規模なモデルには実用的ではありません。PEFTメソッドは、元のモデルサイズの1％未満のパラメータを使用してモデルを適応させるアプローチを導入します。この劇的な学習可能なパラメータの削減により、次のことが可能になります：
+
+- 限られたGPUメモリを持つ消費者向けハードウェアでの微調整
+- 複数のタスク固有の適応を効率的に保存
+- 低データシナリオでのより良い一般化
+- より速いトレーニングと反復サイクル
+
+## 利用可能なメソッド
+
+このモジュールでは、2つの人気のあるPEFTメソッドを紹介します：
+
+### 1️⃣ LoRA (低ランク適応)
+
+LoRAは、効率的なモデル適応のためのエレガントなソリューションを提供する最も広く採用されているPEFTメソッドとして浮上しました。モデル全体を変更する代わりに、**LoRAはモデルの注意層に学習可能な行列を注入します。**このアプローチは、通常、完全な微調整と比較して約90％の学習可能なパラメータを削減しながら、同等の性能を維持します。[LoRA (低ランク適応)](./lora_adapters.md)セクションでLoRAを詳しく見ていきます。
+ 
+### 2️⃣ プロンプトチューニング
+
+プロンプトチューニングは、モデルの重みを変更するのではなく、**入力に学習可能なトークンを追加する**さらに軽量なアプローチを提供します。プロンプトチューニングはLoRAほど人気はありませんが、モデルを新しいタスクやドメインに迅速に適応させるための便利な技術です。[プロンプトチューニング](./prompt_tuning.md)セクションでプロンプトチューニングを詳しく見ていきます。
+
+## 演習ノートブック
+
+| タイトル | 説明 | 演習 | リンク | Colab |
+|-------|-------------|----------|------|-------|
+| LoRA微調整 | LoRAアダプタを使用してモデルを微調整する方法を学ぶ | 🐢 LoRAを使用してモデルをトレーニング<br>🐕 異なるランク値で実験<br>🦁 完全な微調整と性能を比較 | [ノートブック](./notebooks/finetune_sft_peft.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| LoRAアダプタの読み込み | トレーニングされたLoRAアダプタを読み込んで使用する方法を学ぶ | 🐢 事前学習されたアダプタを読み込む<br>🐕 アダプタをベースモデルと統合<br>🦁 複数のアダプタ間を切り替える | [ノートブック](./notebooks/load_lora_adapter.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+<!-- | プロンプトチューニング | プロンプトチューニングを実装する方法を学ぶ | 🐢 ソフトプロンプトをトレーニング<br>🐕 異なる初期化戦略を比較<br>🦁 複数のタスクで評価 | [ノートブック](./notebooks/prompt_tuning_example.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/3_parameter_efficient_finetuning/notebooks/prompt_tuning_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | -->
+
+## リソース
+- [PEFTドキュメント](https://huggingface.co/docs/peft)
+- [LoRA論文](https://arxiv.org/abs/2106.09685)
+- [QLoRA論文](https://arxiv.org/abs/2305.14314)
+- [プロンプトチューニング論文](https://arxiv.org/abs/2104.08691)
+- [Hugging Face PEFTガイド](https://huggingface.co/blog/peft)
+- [2024年にHugging FaceでLLMを微調整する方法](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) 
+- [TRL](https://huggingface.co/docs/trl/index)
diff --git a/ja/3_parameter_efficient_finetuning/lora_adapters.md b/ja/3_parameter_efficient_finetuning/lora_adapters.md
new file mode 100644
index 00000000..1eef41eb
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@@ -0,0 +1,124 @@
+# LoRA (低ランク適応)
+
+LoRAは最も広く採用されているPEFTメソッドとなっています。これは、注意重み行列に小さなランク分解行列を追加することで機能し、通常、学習可能なパラメータを約90％削減します。
+
+## LoRAの理解
+
+LoRA（低ランク適応）は、事前学習されたモデルの重みを固定し、学習可能なランク分解行列をモデルの層に注入するパラメータ効率の良い微調整技術です。微調整中にすべてのモデルパラメータを学習する代わりに、LoRAは低ランク分解を通じて重みの更新を小さな行列に分解し、学習可能なパラメータの数を大幅に削減しながらモデルの性能を維持します。例えば、GPT-3 175Bに適用した場合、LoRAは学習可能なパラメータを10,000倍、GPUメモリ要件を3倍削減しました。LoRAについての詳細は[LoRA論文](https://arxiv.org/pdf/2106.09685)を参照してください。
+
+LoRAは、通常、注意重みに焦点を当てて、トランスフォーマーレイヤーにランク分解行列のペアを追加することで機能します。推論中に、これらのアダプタ重みはベースモデルと統合され、追加の遅延オーバーヘッドが発生しません。LoRAは、大規模な言語モデルを特定のタスクやドメインに適応させるのに特に役立ち、リソース要件を管理可能に保ちます。
+
+## LoRAアダプタの読み込み
+
+アダプタは、load_adapter()を使用して事前学習されたモデルに読み込むことができ、これは重みが統合されていない異なるアダプタを試すのに便利です。set_adapter()関数を使用してアクティブなアダプタ重みを設定します。ベースモデルに戻るには、unload()を使用してすべてのLoRAモジュールをアンロードできます。これにより、異なるタスク固有の重み間の切り替えが容易になります。
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("<base_model_name>")
+peft_model_id = "<peft_adapter_id>"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+```
+
+![lora_load_adapter](./images/lora_adapter.png)
+
+## LoRAアダプタの統合
+
+LoRAでトレーニングした後、アダプタ重みをベースモデルに統合して、デプロイを容易にすることができます。これにより、統合された重みを持つ単一のモデルが作成され、推論中にアダプタを別々に読み込む必要がなくなります。
+
+統合プロセスには、メモリ管理と精度に注意が必要です。ベースモデルとアダプタ重みの両方を同時に読み込む必要があるため、十分なGPU/CPUメモリが利用可能であることを確認してください。`transformers`の`device_map="auto"`を使用すると、自動メモリ管理が容易になります。トレーニング中に使用した精度（例：float16）を一貫して維持し、統合されたモデルを同じ形式で保存してデプロイします。デプロイ前に、アダプタベースのバージョンと出力および性能メトリックを比較して、統合されたモデルを検証してください。
+
+アダプタは、異なるタスクやドメイン間の切り替えにも便利です。ベースモデルとアダプタ重みを別々に読み込むことができます。これにより、異なるタスク固有の重み間の迅速な切り替えが可能になります。
+
+## 実装ガイド
+
+`notebooks/`ディレクトリには、さまざまなPEFTメソッドを実装するための実践的なチュートリアルと演習が含まれています。基本的な紹介には`load_lora_adapter_example.ipynb`を、LoRAとSFTを使用したモデルの微調整について詳しく知りたい場合は`lora_finetuning.ipynb`を参照してください。
+
+PEFTメソッドを実装する際は、LoRAのランク値（4-8）を小さく設定し、トレーニング損失を監視します。検証セットを使用して過学習を防ぎ、可能であればフルファインチューニングのベースラインと結果を比較します。異なるメソッドの有効性はタスクによって異なるため、実験が重要です。
+
+## OLoRA
+
+[OLoRA](https://arxiv.org/abs/2406.01775)は、QR分解を使用してLoRAアダプタを初期化します。OLoRAは、QR分解の係数によってモデルのベース重みを変換します。つまり、トレーニングを行う前に重みを変換します。このアプローチは、安定性を大幅に向上させ、収束速度を加速し、最終的に優れた性能を達成します。
+
+## TRLとPEFTの使用
+
+PEFTメソッドは、TRL（Transformers Reinforcement Learning）と組み合わせて効率的な微調整を行うことができます。この統合は、RLHF（Reinforcement Learning from Human Feedback）に特に有用であり、メモリ要件を削減します。
+
+```python
+from peft import LoraConfig
+from transformers import AutoModelForCausalLM
+
+# PEFT設定でモデルを読み込む
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+# 特定のデバイスにモデルを読み込む
+model = AutoModelForCausalLM.from_pretrained(
+    "your-model-name",
+    load_in_8bit=True,  # オプション: 8ビット精度を使用
+    device_map="auto",
+    peft_config=lora_config
+)
+```
+
+上記では、`device_map="auto"`を使用してモデルを自動的に適切なデバイスに割り当てました。また、`device_map={"": device_index}`を使用してモデルを特定のデバイスに手動で割り当てることもできます。メモリ使用量を効率的に保ちながら、複数のGPUにトレーニングをスケールすることもできます。
+
+## 基本的な統合実装
+
+LoRAアダプタをトレーニングした後、アダプタ重みをベースモデルに統合することができます。以下はその方法です：
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+# 1. ベースモデルを読み込む
+base_model = AutoModelForCausalLM.from_pretrained(
+    "base_model_name",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# 2. アダプタを持つPEFTモデルを読み込む
+peft_model = PeftModel.from_pretrained(
+    base_model,
+    "path/to/adapter",
+    torch_dtype=torch.float16
+)
+
+# 3. アダプタ重みをベースモデルと統合
+try:
+    merged_model = peft_model.merge_and_unload()
+except RuntimeError as e:
+    print(f"統合に失敗しました: {e}")
+    # フォールバック戦略またはメモリ最適化を実装
+
+# 4. 統合されたモデルを保存
+merged_model.save_pretrained("path/to/save/merged_model")
+```
+
+保存されたモデルのサイズに不一致がある場合は、トークナイザーも保存していることを確認してください：
+
+```python
+# モデルとトークナイザーの両方を保存
+tokenizer = AutoTokenizer.from_pretrained("base_model_name")
+merged_model.save_pretrained("path/to/save/merged_model")
+tokenizer.save_pretrained("path/to/save/merged_model")
+```
+
+## 次のステップ
+
+⏩ [プロンプトチューニング](prompt_tuning.md)ガイドに進み、プロンプトチューニングでモデルを微調整する方法を学びましょう。
+⏩ [LoRAアダプタの読み込みチュートリアル](./notebooks/load_lora_adapter.ipynb)に進み、LoRAアダプタを読み込む方法を学びましょう。
+
+# リソース
+
+- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685)
+- [PEFTドキュメント](https://huggingface.co/docs/peft)
+- [Hugging FaceのPEFTに関するブログ記事](https://huggingface.co/blog/peft)
diff --git a/ja/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/ja/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
new file mode 100644
index 00000000..8fea097a
--- /dev/null
+++ b/ja/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb
@@ -0,0 +1,516 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z-6LLOPZouLg"
+   },
+   "source": [
+    "# Hugging Face TRLを使用してLoRAアダプタでLLMを微調整する方法\n",
+    "\n",
+    "このノートブックでは、LoRA（低ランク適応）アダプタを使用して大規模言語モデルを効率的に微調整する方法を示します。LoRAは、次のようなパラメータ効率の良い微調整技術です：\n",
+    "- 事前学習されたモデルの重みを固定\n",
+    "- 注意層に小さな学習可能なランク分解行列を追加\n",
+    "- 通常、学習可能なパラメータを約90％削減\n",
+    "- メモリ効率を維持しながらモデル性能を維持\n",
+    "\n",
+    "以下の内容をカバーします：\n",
+    "1. 開発環境のセットアップとLoRA設定\n",
+    "2. アダプタトレーニング用のデータセットの作成と準備\n",
+    "3. `trl`と`SFTTrainer`を使用してLoRAアダプタで微調整\n",
+    "4. モデルのテストとアダプタの統合（オプション）\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fXqd9BXgouLi"
+   },
+   "source": [
+    "## 1. 開発環境のセットアップ\n",
+    "\n",
+    "最初のステップは、Hugging FaceライブラリとPytorchをインストールすることです。`trl`、`transformers`、`datasets`を含みます。`trl`について聞いたことがない場合でも心配ありません。これは`transformers`と`datasets`の上に構築された新しいライブラリで、微調整、RLHF、オープンLLMの調整を容易にします。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tKvGVxImouLi"
+   },
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件のインストール\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Hugging Faceへの認証\n",
+    "\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# 便利のため、Hugging Faceのトークンを環境変数HF_TOKENとして設定できます"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XHUzfwpKouLk"
+   },
+   "source": [
+    "## 2. データセットの読み込み"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 2260\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['full_topic', 'messages'],\n",
+       "        num_rows: 119\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# サンプルデータセットの読み込み\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: パスと名前のパラメータを使用してデータセットと設定を定義\n",
+    "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TOhJdtsouLk"
+   },
+   "source": [
+    "## 3. `trl`と`SFTTrainer`を使用してLLMをLoRAで微調整\n",
+    "\n",
+    "`trl`の[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer)は、[PEFT](https://huggingface.co/docs/peft/en/index)ライブラリを通じてLoRAアダプタとの統合を提供します。このセットアップの主な利点は次のとおりです：\n",
+    "\n",
+    "1. **メモリ効率**：\n",
+    "   - アダプタパラメータのみがGPUメモリに保存されます\n",
+    "   - ベースモデルの重みは固定され、低精度で読み込むことができます\n",
+    "   - 大規模モデルの消費者向けGPUでの微調整が可能\n",
+    "\n",
+    "2. **トレーニング機能**：\n",
+    "   - 最小限のセットアップでネイティブPEFT/LoRA統合\n",
+    "   - さらにメモリ効率を向上させるためのQLoRA（量子化LoRA）サポート\n",
+    "\n",
+    "3. **アダプタ管理**：\n",
+    "   - チェックポイント中のアダプタ重みの保存\n",
+    "   - ベースモデルにアダプタを統合する機能\n",
+    "\n",
+    "例としてLoRAを使用します。これは、LoRAと4ビット量子化を組み合わせて、性能を犠牲にせずにメモリ使用量をさらに削減します。セットアップには次の手順が必要です：\n",
+    "1. LoRA設定（ランク、アルファ、ドロップアウト）を定義\n",
+    "2. PEFT設定でSFTTrainerを作成\n",
+    "3. アダプタ重みをトレーニングして保存\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 必要なライブラリをインポート\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+    "import torch\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "# モデルとトークナイザーを読み込む\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=model_name\n",
+    ").to(device)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+    "\n",
+    "# チャット形式を設定\n",
+    "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# 微調整の名前を設定\n",
+    "finetune_name = \"SmolLM2-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZbuVArTHouLk"
+   },
+   "source": [
+    "`SFTTrainer`は、`peft`とのネイティブ統合をサポートしており、例えばLoRAを使用してLLMを効率的に微調整するのが非常に簡単です。`LoraConfig`を作成し、トレーナーに提供するだけです。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>演習: 微調整のためのLoRAパラメータを定義</h2>\n",
+    "    <p>Hugging Faceのハブからデータセットを取得し、それを使用してモデルを微調整します。</p>\n",
+    "    <p><b>難易度レベル</b></p>\n",
+    "    <p>🐢 一般的なパラメータを使用して任意の微調整を行う</p>\n",
+    "    <p>🐕 パラメータを調整し、重みとバイアスでレビューする</p>\n",
+    "    <p>🦁 パラメータを調整し、推論結果の変化を示す</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "blDSs9swouLk"
+   },
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig\n",
+    "\n",
+    "# TODO: LoRAパラメータを設定\n",
+    "# r: LoRA更新行列のランク次元（小さいほど圧縮率が高い）\n",
+    "rank_dimension = 6\n",
+    "# lora_alpha: LoRA層のスケーリングファクター（高いほど適応が強い）\n",
+    "lora_alpha = 8\n",
+    "# lora_dropout: LoRA層のドロップアウト確率（過学習を防ぐのに役立つ）\n",
+    "lora_dropout = 0.05\n",
+    "\n",
+    "peft_config = LoraConfig(\n",
+    "    r=rank_dimension,  # ランク次元 - 通常4-32の範囲\n",
+    "    lora_alpha=lora_alpha,  # LoRAスケーリングファクター - 通常ランクの2倍\n",
+    "    lora_dropout=lora_dropout,  # LoRA層のドロップアウト確率\n",
+    "    bias=\"none\",  # LoRAのバイアスタイプ。対応するバイアスはトレーニング中に更新されます。\n",
+    "    target_modules=\"all-linear\",  # LoRAを適用するモジュール\n",
+    "    task_type=\"CAUSAL_LM\",  # モデルアーキテクチャのタスクタイプ\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l5NUDPcaouLl"
+   },
+   "source": [
+    "トレーニングを開始する前に、使用するハイパーパラメータ（`TrainingArguments`）を定義する必要があります。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NqT28VZlouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# トレーニング設定\n",
+    "# QLoRA論文の推奨に基づくハイパーパラメータ\n",
+    "args = SFTConfig(\n",
+    "    # 出力設定\n",
+    "    output_dir=finetune_name,  # モデルチェックポイントを保存するディレクトリ\n",
+    "    # トレーニング期間\n",
+    "    num_train_epochs=1,  # トレーニングエポック数\n",
+    "    # バッチサイズ設定\n",
+    "    per_device_train_batch_size=2,  # GPUごとのバッチサイズ\n",
+    "    gradient_accumulation_steps=2,  # 大きな効果的なバッチのための勾配蓄積\n",
+    "    # メモリ最適化\n",
+    "    gradient_checkpointing=True,  # メモリ節約のための計算トレードオフ\n",
+    "    # オプティマイザ設定\n",
+    "    optim=\"adamw_torch_fused\",  # 効率のために融合されたAdamWを使用\n",
+    "    learning_rate=2e-4,  # 学習率（QLoRA論文）\n",
+    "    max_grad_norm=0.3,  # 勾配クリッピングの閾値\n",
+    "    # 学習率スケジュール\n",
+    "    warmup_ratio=0.03,  # ウォームアップのステップの割合\n",
+    "    lr_scheduler_type=\"constant\",  # ウォームアップ後に学習率を一定に保つ\n",
+    "    # ロギングと保存\n",
+    "    logging_steps=10,  # Nステップごとにメトリックをログ\n",
+    "    save_strategy=\"epoch\",  # 各エポックごとにチェックポイントを保存\n",
+    "    # 精度設定\n",
+    "    bf16=True,  # bfloat16精度を使用\n",
+    "    # 統合設定\n",
+    "    push_to_hub=False,  # HuggingFace Hubにプッシュしない\n",
+    "    report_to=\"none\",  # 外部ロギングを無効化\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cGhR7uFBouLl"
+   },
+   "source": [
+    "すべてのビルディングブロックが揃ったので、`SFTTrainer`を作成してモデルのトレーニングを開始します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "M00Har2douLl"
+   },
+   "outputs": [],
+   "source": [
+    "max_seq_length = 1512  # モデルとデータセットのパッキングの最大シーケンス長\n",
+    "\n",
+    "# LoRA設定でSFTTrainerを作成\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    peft_config=peft_config,  # LoRA設定\n",
+    "    max_seq_length=max_seq_length,  # 最大シーケンス長\n",
+    "    tokenizer=tokenizer,\n",
+    "    packing=True,  # 効率のために入力パッキングを有効化\n",
+    "    dataset_kwargs={\n",
+    "        \"add_special_tokens\": False,  # テンプレートで処理される特殊トークン\n",
+    "        \"append_concat_token\": False,  # 追加のセパレータは不要\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zQ_kRN24ouLl"
+   },
+   "source": [
+    "トレーニングを開始するには、`Trainer`インスタンスの`train()`メソッドを呼び出します。これにより、トレーニングループが開始され、モデルが3エポックにわたってトレーニングされます。PEFTメソッドを使用しているため、適応されたモデルの重みのみを保存し、完全なモデルは保存しません。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Tq4nIYqKouLl"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "300e5dfbb4b54750b77324345c7591f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/72 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=72, training_loss=1.6402628521124523, metrics={'train_runtime': 195.2398, 'train_samples_per_second': 1.485, 'train_steps_per_second': 0.369, 'total_flos': 282267289092096.0, 'train_loss': 1.6402628521124523, 'epoch': 0.993103448275862})"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# トレーニングを開始し、モデルは自動的にハブと出力ディレクトリに保存されます\n",
+    "trainer.train()\n",
+    "\n",
+    "# モデルを保存\n",
+    "trainer.save_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y4HHSYYzouLl"
+   },
+   "source": [
+    "Flash Attentionを使用して15kサンプルのデータセットで3エポックのトレーニングを行った結果、`g5.2xlarge`で4:14:36かかりました。このインスタンスのコストは`1.21$/h`で、合計コストは約`5.3$`です。\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C309KsXjouLl"
+   },
+   "source": [
+    "### LoRAアダプタを元のモデルに統合\n",
+    "\n",
+    "LoRAを使用する場合、トレーニング中はアダプタ重みのみを学習し、ベースモデルは固定されます。トレーニング中は、これらの軽量なアダプタ重み（約2-10MB）のみを保存し、完全なモデルのコピーは保存しません。ただし、デプロイのためにアダプタをベースモデルに統合することを検討するかもしれません：\n",
+    "\n",
+    "1. **デプロイの簡素化**：ベースモデル+アダプタの代わりに単一のモデルファイル\n",
+    "2. **推論速度**：アダプタ計算のオーバーヘッドなし\n",
+    "3. **フレームワークの互換性**：サービングフレームワークとの互換性向上\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import AutoPeftModelForCausalLM\n",
+    "\n",
+    "\n",
+    "# CPU上でPEFTモデルを読み込む\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    pretrained_model_name_or_path=args.output_dir,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    low_cpu_mem_usage=True,\n",
+    ")\n",
+    "\n",
+    "# LoRAとベースモデルを統合して保存\n",
+    "merged_model = model.merge_and_unload()\n",
+    "merged_model.save_pretrained(\n",
+    "    args.output_dir, safe_serialization=True, max_shard_size=\"2GB\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yO6E9quouLl"
+   },
+   "source": [
+    "## 3. モデルをテストして推論を実行\n",
+    "\n",
+    "トレーニングが完了したら、モデルをテストしたいと思います。元のデータセットから異なるサンプルを読み込み、単純なループと精度をメトリックとして使用してモデルを評価します。\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>ボーナス演習: LoRAアダプタの読み込み</h2>\n",
+    "    <p>例のノートブックから学んだことを使用して、トレーニングされたLoRAアダプタを推論のために読み込みます。</p> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "I5B494OdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "# メモリを再度解放\n",
+    "del model\n",
+    "del trainer\n",
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "P1UhohVdouLl"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from peft import AutoPeftModelForCausalLM\n",
+    "from transformers import AutoTokenizer, pipeline\n",
+    "\n",
+    "# PEFTアダプタでモデルを読み込む\n",
+    "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n",
+    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n",
+    ")\n",
+    "pipe = pipeline(\n",
+    "    \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "99uFDAuuouLl"
+   },
+   "source": [
+    "いくつかのプロンプトサンプルをテストし、モデルの性能を確認しましょう。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "-shSmUbvouLl",
+    "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824"
+   },
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"ドイツの首都はどこですか？その理由と過去に異なっていたかどうかを説明してください。\",\n",
+    "    \"数の階乗を計算するPython関数を書いてください。\",\n",
+    "    \"長さ25フィート、幅15フィートの長方形の庭があります。庭全体を囲むフェンスを作りたい場合、何フィートのフェンスが必要ですか？\",\n",
+    "    \"果物と野菜の違いは何ですか？それぞれの例を挙げてください。\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def test_inference(prompt):\n",
+    "    prompt = pipe.tokenizer.apply_chat_template(\n",
+    "        [{\"role\": \"user\", \"content\": prompt}],\n",
+    "        tokenize=False,\n",
+    "        add_generation_prompt=True,\n",
+    "    )\n",
+    "    outputs = pipe(\n",
+    "        prompt,\n",
+    "    )\n",
+    "    return outputs[0][\"generated_text\"][len(prompt) :].strip()\n",
+    "\n",
+    "\n",
+    "for prompt in prompts:\n",
+    "    print(f\"    prompt:\\n{prompt}\")\n",
+    "    print(f\"    response:\\n{test_inference(prompt)}\")\n",
+    "    print(\"-\" * 50)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ja/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/ja/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
new file mode 100644
index 00000000..6ef9dd8c
--- /dev/null
+++ b/ja/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb
@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z-6LLOPZouLg"
+   },
+   "source": [
+    "# LoRAアダプタの読み込み\n",
+    "\n",
+    "このノートブックでは、事前学習されたモデルにLoRAアダプタを読み込む方法を示します。LoRA（低ランク適応）は、事前学習されたモデルの重みを固定し、学習可能なランク分解行列をモデルの層に注入するパラメータ効率の良い微調整技術です。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fXqd9BXgouLi"
+   },
+   "source": [
+    "## 1. 開発環境のセットアップ\n",
+    "\n",
+    "最初のステップは、Hugging FaceライブラリとPytorchをインストールすることです。`trl`、`transformers`、`datasets`を含みます。`trl`について聞いたことがない場合でも心配ありません。これは`transformers`と`datasets`の上に構築された新しいライブラリで、微調整、RLHF、オープンLLMの調整を容易にします。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tKvGVxImouLi"
+   },
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件のインストール\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Hugging Faceへの認証\n",
+    "\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# 便利のため、Hugging Faceのトークンを環境変数HF_TOKENとして設定できます"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XHUzfwpKouLk"
+   },
+   "source": [
+    "## 2. モデルとアダプタの読み込み"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Downloading (…)okenizer_config.json: 100%|██████████| 28.0/28.0 [00:00<00:00, 28.0kB/s]\n",
+       "Downloading (…)lve/main/config.json: 100%|██████████| 1.11k/1.11k [00:00<00:00, 1.11MB/s]\n",
+       "Downloading (…)olve/main/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 1.06MB/s]\n",
+       "Downloading (…)olve/main/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 525kB/s]\n",
+       "Downloading (…)/main/tokenizer.json: 100%|██████████| 2.13M/2.13M [00:00<00:00, 2.13MB/s]\n",
+       "Downloading (…)/main/adapter_config.json: 100%|██████████| 472/472 [00:00<00:00, 472kB/s]\n",
+       "Downloading (…)/adapter_model.bin: 100%|██████████| 1.22G/1.22G [00:10<00:00, 120MB/s]\n",
+       "Downloading pytorch_model.bin: 100%|██████████| 1.22G/1.22G [00:10<00:00, 120MB/s]\n"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# モデルとアダプタの読み込み\n",
+    "from transformers import AutoModelForCausalLM\n",
+    "from peft import PeftModel\n",
+    "\n",
+    "base_model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-135M\")\n",
+    "peft_model_id = \"HuggingFaceTB/SmolLM2-135M-LoRA\"\n",
+    "model = PeftModel.from_pretrained(base_model, peft_model_id)\n",
+    "model.eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TOhJdtsouLk"
+   },
+   "source": [
+    "## 3. モデルのテスト\n",
+    "\n",
+    "モデルが正しく読み込まれたことを確認するために、いくつかのサンプル入力でテストします。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Downloading (…)okenizer_config.json: 100%|██████████| 28.0/28.0 [00:00<00:00, 28.0kB/s]\n",
+       "Downloading (…)lve/main/config.json: 100%|██████████| 1.11k/1.11k [00:00<00:00, 1.11MB/s]\n",
+       "Downloading (…)olve/main/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 1.06MB/s]\n",
+       "Downloading (…)olve/main/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 525kB/s]\n",
+       "Downloading (…)/main/tokenizer.json: 100%|██████████| 2.13M/2.13M [00:00<00:00, 2.13MB/s]\n"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# トークナイザーの読み込み\n",
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"HuggingFaceTB/SmolLM2-135M\")\n",
+    "\n",
+    "# サンプル入力の生成\n",
+    "inputs = tokenizer(\"こんにちは、元気ですか？\", return_tensors=\"pt\")\n",
+    "outputs = model.generate(**inputs, max_new_tokens=50)\n",
+    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ja/3_parameter_efficient_finetuning/prompt_tuning.md b/ja/3_parameter_efficient_finetuning/prompt_tuning.md
new file mode 100644
index 00000000..2676bbf6
--- /dev/null
+++ b/ja/3_parameter_efficient_finetuning/prompt_tuning.md
@@ -0,0 +1,74 @@
+# プロンプトチューニング
+
+プロンプトチューニングは、モデルの重みではなく入力表現を変更するパラメータ効率の良いアプローチです。従来の微調整がすべてのモデルパラメータを更新するのに対し、プロンプトチューニングはベースモデルを固定したまま、少数の学習可能なトークンを追加して最適化します。
+
+## プロンプトチューニングの理解
+
+プロンプトチューニングは、学習可能な連続ベクトル（ソフトプロンプト）を入力テキストの前に追加するパラメータ効率の良い微調整方法です。従来の離散テキストプロンプトとは異なり、これらのソフトプロンプトはベースモデルを固定したまま、逆伝播を通じて学習されます。この方法は、["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691)（Lester et al., 2021）で紹介され、モデルサイズが大きくなるにつれてプロンプトチューニングがモデル微調整と競争力を持つようになることが示されました。論文内では、約100億パラメータのモデルで、プロンプトチューニングがモデル微調整の性能に匹敵し、タスクごとに数百のパラメータしか変更しないことが示されています。
+
+これらのソフトプロンプトは、トレーニング中に最適化されるモデルの埋め込み空間内の連続ベクトルです。従来の自然言語トークンを使用する離散プロンプトとは異なり、ソフトプロンプトは固有の意味を持たず、勾配降下を通じて固定モデルから望ましい動作を引き出すことを学習します。この技術は、各タスクに対して小さなプロンプトベクトル（通常は数百パラメータ）を保存するだけで済むため、マルチタスクシナリオに特に効果的です。このアプローチは、最小限のメモリフットプリントを維持するだけでなく、モデルの再読み込みなしにプロンプトベクトルを交換するだけで迅速なタスク切り替えを可能にします。
+
+## トレーニングプロセス
+
+ソフトプロンプトは通常、8〜32トークンで構成され、ランダムに初期化するか、既存のテキストから初期化されます。初期化方法はトレーニングプロセスにおいて重要な役割を果たし、テキストベースの初期化はランダム初期化よりも優れた性能を発揮することがよくあります。
+
+トレーニング中は、プロンプトパラメータのみが更新され、ベースモデルは固定されたままです。この集中アプローチは標準的なトレーニング目標を使用しますが、プロンプトトークンの学習率と勾配の挙動に注意を払う必要があります。
+
+## PEFTを使用した実装
+
+PEFTライブラリを使用すると、プロンプトチューニングの実装が簡単になります。以下は基本的な例です：
+
+```python
+from peft import PromptTuningConfig, TaskType, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# ベースモデルを読み込む
+model = AutoModelForCausalLM.from_pretrained("your-base-model")
+tokenizer = AutoTokenizer.from_pretrained("your-base-model")
+
+# プロンプトチューニングを設定
+peft_config = PromptTuningConfig(
+    task_type=TaskType.CAUSAL_LM,
+    num_virtual_tokens=8,  # 学習可能なトークンの数
+    prompt_tuning_init="TEXT",  # テキストから初期化
+    prompt_tuning_init_text="このテキストがポジティブかネガティブかを分類してください：",
+    tokenizer_name_or_path="your-base-model",
+)
+
+# プロンプトチューニング可能なモデルを作成
+model = get_peft_model(model, peft_config)
+```
+
+## 他の方法との比較
+
+他のPEFTアプローチと比較すると、プロンプトチューニングはその効率性で際立っています。LoRAは低パラメータ数とメモリ使用量を提供しますが、タスク切り替えにはアダプタの読み込みが必要です。プロンプトチューニングはさらに低いリソース使用量を達成し、即時のタスク切り替えを可能にします。対照的に、フルファインチューニングは多くのリソースを必要とし、異なるタスクごとに別々のモデルコピーが必要です。
+
+| 方法 | パラメータ | メモリ | タスク切り替え |
+|--------|------------|---------|----------------|
+| プロンプトチューニング | 非常に低い | 最小限 | 簡単 |
+| LoRA | 低い | 低い | 読み込みが必要 |
+| フルファインチューニング | 高い | 高い | 新しいモデルコピー |
+
+プロンプトチューニングを実装する際は、最初に少数の仮想トークン（8〜16）を使用し、タスクの複雑さが要求する場合にのみ増やします。タスクに関連するテキストを使用したテキスト初期化は、ランダム初期化よりも優れた結果をもたらすことがよくあります。初期化戦略は、ターゲットタスクの複雑さを反映する必要があります。
+
+トレーニングには、フルファインチューニングとは異なる考慮事項が必要です。高い学習率が効果的なことが多いですが、プロンプトトークンの勾配を注意深く監視することが重要です。多様な例で定期的に検証することで、さまざまなシナリオでの堅牢な性能を確保します。
+
+## 応用
+
+プロンプトチューニングは、次のようなシナリオで優れた効果を発揮します：
+
+1. マルチタスク展開
+2. リソース制約のある環境
+3. 迅速なタスク適応
+4. プライバシーに敏感なアプリケーション
+
+モデルが小さくなるにつれて、プロンプトチューニングはフルファインチューニングと比較して競争力が低下します。例えば、SmolLM2のようなモデルでは、プロンプトチューニングはフルファインチューニングよりも関連性が低くなります。
+
+## 次のステップ
+
+⏭️ [LoRAアダプタのチュートリアル](./notebooks/finetune_sft_peft.ipynb)に進み、LoRAアダプタでモデルを微調整する方法を学びましょう。
+
+## リソース
+- [PEFTドキュメント](https://huggingface.co/docs/peft)
+- [プロンプトチューニング論文](https://arxiv.org/abs/2104.08691)
+- [Hugging Face Cookbook](https://huggingface.co/learn/cookbook/prompt_tuning_peft)
diff --git a/ja/4_evaluation/README.md b/ja/4_evaluation/README.md
new file mode 100644
index 00000000..bb3bfafd
--- /dev/null
+++ b/ja/4_evaluation/README.md
@@ -0,0 +1,39 @@
+# 評価
+
+評価は、言語モデルの開発と展開において重要なステップです。評価は、モデルがさまざまな能力にわたってどれだけうまく機能するかを理解し、改善の余地を特定するのに役立ちます。このモジュールでは、標準ベンチマークとドメイン固有の評価アプローチの両方をカバーし、smolモデルを包括的に評価します。
+
+Hugging Faceが開発した強力な評価ライブラリである[`lighteval`](https://github.com/huggingface/lighteval)を使用します。評価の概念とベストプラクティスについて詳しく知りたい場合は、評価[ガイドブック](https://github.com/huggingface/evaluation-guidebook)を参照してください。
+
+## モジュール概要
+
+徹底した評価戦略は、モデル性能の複数の側面を検討します。質問応答や要約などのタスク固有の能力を評価し、モデルがさまざまなタイプの問題にどれだけうまく対処できるかを理解します。出力の品質を一貫性や事実の正確性などの要素で測定します。安全性評価は、潜在的な有害な出力やバイアスを特定するのに役立ちます。最後に、ドメインの専門知識テストは、ターゲット分野でのモデルの専門知識を検証します。
+
+## コンテンツ
+
+### 1️⃣ [自動ベンチマーク](./automatic_benchmarks.md)
+
+標準化されたベンチマークとメトリクスを使用してモデルを評価する方法を学びます。MMLUやTruthfulQAなどの一般的なベンチマークを探求し、主要な評価メトリクスと設定を理解し、再現可能な評価のベストプラクティスをカバーします。
+
+### 2️⃣ [カスタムドメイン評価](./custom_evaluation.md)
+
+特定のユースケースに合わせた評価パイプラインを作成する方法を学びます。カスタム評価タスクの設計、専門的なメトリクスの実装、要件に合った評価データセットの構築について説明します。
+
+### 3️⃣ [ドメイン評価プロジェクト](./project/README.md)
+
+ドメイン固有の評価パイプラインを構築する完全な例を紹介します。評価データセットの生成、データ注釈のためのArgillaの使用、標準化されたデータセットの作成、LightEvalを使用したモデルの評価方法を学びます。
+
+### 演習ノートブック
+
+| タイトル | 説明 | 演習 | リンク | Colab |
+|-------|-------------|----------|------|-------|
+| LLMの評価と分析 | LightEvalを使用して特定のドメインでモデルを評価および比較する方法を学びます | 🐢 医療ドメインタスクを使用してモデルを評価する <br> 🐕 異なるMMLUタスクで新しいドメイン評価を作成する <br> 🦁 ドメイン固有のカスタム評価タスクを作成する | [ノートブック](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## リソース
+
+- [評価ガイドブック](https://github.com/huggingface/evaluation-guidebook) - LLM評価の包括的なガイド
+- [LightEvalドキュメント](https://github.com/huggingface/lighteval) - LightEvalライブラリの公式ドキュメント
+- [Argillaドキュメント](https://docs.argilla.io) - Argillaアノテーションプラットフォームについて学ぶ
+- [MMLU論文](https://arxiv.org/abs/2009.03300) - MMLUベンチマークを説明する論文
+- [カスタムタスクの作成](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [カスタムメトリクスの作成](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [既存のメトリクスの使用](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/ja/4_evaluation/automatic_benchmarks.md b/ja/4_evaluation/automatic_benchmarks.md
new file mode 100644
index 00000000..81df5393
--- /dev/null
+++ b/ja/4_evaluation/automatic_benchmarks.md
@@ -0,0 +1,131 @@
+# 自動ベンチマーク
+
+自動ベンチマークは、さまざまなタスクや能力にわたって言語モデルを評価するための標準化されたツールとして機能します。これらはモデルの性能を理解するための有用な出発点を提供しますが、包括的な評価戦略の一部に過ぎないことを認識することが重要です。
+
+## 自動ベンチマークの理解
+
+自動ベンチマークは通常、事前に定義されたタスクと評価指標を持つキュレーションされたデータセットで構成されます。これらのベンチマークは、基本的な言語理解から複雑な推論まで、モデルのさまざまな側面を評価することを目的としています。自動ベンチマークの主な利点はその標準化にあります。これにより、異なるモデル間で一貫した比較が可能となり、再現性のある結果が得られます。
+
+ただし、ベンチマークの性能が必ずしも実世界での有効性に直結するわけではないことを理解することが重要です。学術的なベンチマークで優れた成績を収めたモデルでも、特定のドメインアプリケーションや実際のユースケースでは苦労することがあります。
+
+## ベンチマークとその限界
+
+### 一般知識ベンチマーク
+
+MMLU（Massive Multitask Language Understanding）は、科学から人文科学まで57の科目にわたる知識をテストします。包括的ではありますが、特定のドメインに必要な専門知識の深さを反映しているわけではありません。TruthfulQAは、モデルが一般的な誤解を再現する傾向を評価しますが、すべての形態の誤情報を捉えることはできません。
+
+### 推論ベンチマーク
+BBH（Big Bench Hard）とGSM8Kは、複雑な推論タスクに焦点を当てています。BBHは論理的思考と計画をテストし、GSM8Kは特に数学的な問題解決を対象としています。これらのベンチマークは分析能力を評価するのに役立ちますが、実世界のシナリオで必要とされる微妙な推論を完全に捉えることはできません。
+
+### 言語理解
+HELMは包括的な評価フレームワークを提供し、WinoGrandeは代名詞の曖昧性解消を通じて常識をテストします。これらのベンチマークは言語処理能力に関する洞察を提供しますが、自然な会話やドメイン固有の用語の複雑さを完全に表現することはできません。
+
+## 代替評価アプローチ
+
+多くの組織は、標準ベンチマークの限界に対処するために代替評価方法を開発しています：
+
+### LLM-as-Judge
+ある言語モデルを使用して別のモデルの出力を評価する方法がますます人気を集めています。このアプローチは、従来の指標よりも微妙なフィードバックを提供することができますが、それ自体のバイアスと限界も伴います。
+
+### 評価アリーナ
+AnthropicのConstitutional AI Arenaのようなプラットフォームでは、モデルが相互に対話し、制御された環境で互いを評価することができます。これにより、従来のベンチマークでは明らかにならない強みと弱みが明らかになります。
+
+### カスタムベンチマークスイート
+組織は、特定のニーズやユースケースに合わせた内部ベンチマークスイートを開発することがよくあります。これには、ドメイン固有の知識テストや実際の展開条件を反映した評価シナリオが含まれることがあります。
+
+## 独自の評価戦略の作成
+
+LightEvalを使用して標準ベンチマークを実行するのは簡単ですが、ユースケースに特化した評価方法の開発にも時間を投資する必要があります。
+
+標準ベンチマークは有用なベースラインを提供しますが、それだけが評価方法であってはなりません。より包括的なアプローチを開発する方法は次のとおりです：
+
+1. 関連する標準ベンチマークから始めて、ベースラインを確立し、他のモデルとの比較を可能にします。
+
+2. ユースケースの特定の要件と課題を特定します。モデルが実際に実行するタスクは何ですか？どのようなエラーが最も問題になりますか？
+
+3. 実際のユースケースを反映したカスタム評価データセットを開発します。これには次のようなものが含まれるかもしれません：
+   - ドメインからの実際のユーザークエリ
+   - 遭遇した一般的なエッジケース
+   - 特に挑戦的なシナリオの例
+
+4. 多層評価戦略の実装を検討します：
+   - クイックフィードバックのための自動指標
+   - 微妙な理解のための人間の評価
+   - 専門家によるレビュー
+   - 制御された環境でのA/Bテスト
+
+## LightEvalを使用したベンチマーク
+
+LightEvalタスクは特定の形式を使用して定義されます：
+```
+{suite}|{task}|{num_few_shot}|{auto_reduce}
+```
+
+- **suite**: ベンチマークスイート（例：'mmlu', 'truthfulqa'）
+- **task**: スイート内の特定のタスク（例：'abstract_algebra'）
+- **num_few_shot**: プロンプトに含める例の数（ゼロショットの場合は0）
+- **auto_reduce**: プロンプトが長すぎる場合に少数ショットの例を自動的に削減するかどうか（0または1）
+
+例：`"mmlu|abstract_algebra|0|0"`は、ゼロショット推論でMMLUの抽象代数学タスクを評価します。
+
+### 評価パイプラインの例
+
+特定のドメインに関連する自動ベンチマークの評価を設定して実行する完全な例を次に示します：
+
+```python
+from lighteval.tasks import Task, Pipeline
+from transformers import AutoModelForCausalLM
+
+# 評価するタスクを定義
+domain_tasks = [
+    "mmlu|anatomy|0|0",
+    "mmlu|high_school_biology|0|0", 
+    "mmlu|high_school_chemistry|0|0",
+    "mmlu|professional_medicine|0|0"
+]
+
+# パイプラインパラメータを設定
+pipeline_params = {
+    "max_samples": 40,  # 評価するサンプル数
+    "batch_size": 1,    # 推論のバッチサイズ
+    "num_workers": 4    # ワーカープロセスの数
+}
+
+# 評価トラッカーを作成
+evaluation_tracker = EvaluationTracker(
+    output_path="./results",
+    save_generations=True
+)
+
+# モデルを読み込み、パイプラインを作成
+model = AutoModelForCausalLM.from_pretrained("your-model-name")
+pipeline = Pipeline(
+    tasks=domain_tasks,
+    pipeline_parameters=pipeline_params,
+    evaluation_tracker=evaluation_tracker,
+    model=model
+)
+
+# 評価を実行
+pipeline.evaluate()
+
+# 結果を取得して表示
+results = pipeline.get_results()
+pipeline.show_results()
+```
+
+結果は次のような表形式で表示されます：
+```
+|                  Task                  |Version|Metric|Value |   |Stderr|
+|----------------------------------------|------:|------|-----:|---|-----:|
+|all                                     |       |acc   |0.3333|±  |0.1169|
+|leaderboard:mmlu:_average:5             |       |acc   |0.3400|±  |0.1121|
+|leaderboard:mmlu:anatomy:5              |      0|acc   |0.4500|±  |0.1141|
+|leaderboard:mmlu:high_school_biology:5  |      0|acc   |0.1500|±  |0.0819|
+```
+
+結果をpandas DataFrameで処理し、視覚化や表現を自由に行うこともできます。
+
+# 次のステップ
+
+⏩ [カスタムドメイン評価](./custom_evaluation.md)を探索し、特定のニーズに合わせた評価パイプラインの作成方法を学びましょう
diff --git a/ja/4_evaluation/custom_evaluation.md b/ja/4_evaluation/custom_evaluation.md
new file mode 100644
index 00000000..4dc41642
--- /dev/null
+++ b/ja/4_evaluation/custom_evaluation.md
@@ -0,0 +1,132 @@
+# カスタムドメイン評価
+
+標準ベンチマークは貴重な洞察を提供しますが、多くのアプリケーションでは特定のドメインやユースケースに合わせた評価アプローチが必要です。このガイドでは、ターゲットドメインでモデルの性能を正確に評価するためのカスタム評価パイプラインを作成する方法を説明します。
+
+## 評価戦略の設計
+
+成功するカスタム評価戦略は、明確な目標から始まります。ドメインでモデルが示すべき具体的な能力を考慮してください。これには、技術的な知識、推論パターン、ドメイン固有の形式が含まれるかもしれません。これらの要件を慎重に文書化してください。これがタスク設計とメトリック選択の両方を導きます。
+
+評価は、標準的なユースケースとエッジケースの両方をテストする必要があります。例えば、医療ドメインでは、一般的な診断シナリオと稀な状態の両方を評価するかもしれません。金融アプリケーションでは、通常の取引と複数の通貨や特別な条件を含む複雑なエッジケースの両方をテストするかもしれません。
+
+## LightEvalを使用した実装
+
+LightEvalは、カスタム評価を実装するための柔軟なフレームワークを提供します。カスタムタスクを作成する方法は次のとおりです：
+
+```python
+from lighteval.tasks import Task, Doc
+from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase
+
+class CustomEvalTask(Task):
+    def __init__(self):
+        super().__init__(
+            name="custom_task",
+            version="0.0.1",
+            metrics=["accuracy", "f1"],  # 選択したメトリック
+            description="カスタム評価タスクの説明"
+        )
+    
+    def get_prompt(self, sample):
+        # 入力をプロンプトにフォーマット
+        return f"質問: {sample['question']}\n回答:"
+    
+    def process_response(self, response, ref):
+        # モデルの出力を処理し、参照と比較
+        return response.strip() == ref.strip()
+```
+
+## カスタムメトリック
+
+ドメイン固有のタスクには、専門的なメトリックが必要なことがよくあります。LightEvalは、ドメインに関連する性能の側面を捉えるカスタムメトリックを作成するための柔軟なフレームワークを提供します：
+
+```python
+from aenum import extend_enum
+from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping
+import numpy as np
+
+# サンプルレベルのメトリック関数を定義
+def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict:
+    """サンプルごとに複数のスコアを返す例のメトリック"""
+    response = predictions[0]
+    return {
+        "accuracy": response == formatted_doc.choices[formatted_doc.gold_index],
+        "length_match": len(response) == len(formatted_doc.reference)
+    }
+
+# サンプルごとに複数の値を返すメトリックを作成
+custom_metric_group = SampleLevelMetricGrouping(
+    metric_name=["accuracy", "length_match"],  # サブメトリックの名前
+    higher_is_better={  # 各メトリックで高い値が良いかどうか
+        "accuracy": True,
+        "length_match": True
+    },
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=custom_metric,
+    corpus_level_fn={  # 各メトリックを集計する方法
+        "accuracy": np.mean,
+        "length_match": np.mean
+    }
+)
+
+# LightEvalにメトリックを登録
+extend_enum(Metrics, "custom_metric_name", custom_metric_group)
+```
+
+サンプルごとに1つのメトリック値のみが必要な場合の簡単なケース：
+
+```python
+def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
+    """サンプルごとに単一のスコアを返す例のメトリック"""
+    response = predictions[0]
+    return response == formatted_doc.choices[formatted_doc.gold_index]
+
+simple_metric_obj = SampleLevelMetric(
+    metric_name="simple_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.CUSTOM,
+    use_case=MetricUseCase.SCORING,
+    sample_level_fn=simple_metric,
+    corpus_level_fn=np.mean  # サンプル全体で集計する方法
+)
+
+extend_enum(Metrics, "simple_metric", simple_metric_obj)
+```
+
+カスタムメトリックを評価タスクで参照することで、評価タスクで自動的に計算され、指定された関数に従って集計されます。
+
+より複雑なメトリックの場合、次のことを検討してください：
+- フォーマットされたドキュメントのメタデータを使用してスコアを重み付けまたは調整
+- コーパスレベルの統計のためのカスタム集計関数の実装
+- メトリック入力の検証チェックの追加
+- エッジケースと期待される動作の文書化
+
+これらの概念を実装する完全な例については、[ドメイン評価プロジェクト](./project/README.md)を参照してください。
+
+## データセットの作成
+
+高品質の評価には、慎重にキュレーションされたデータセットが必要です。データセット作成のアプローチを次に示します：
+
+1. 専門家のアノテーション：ドメインの専門家と協力して評価例を作成および検証します。[Argilla](https://github.com/argilla-io/argilla)のようなツールを使用すると、このプロセスがより効率的になります。
+
+2. 実世界のデータ：実際の使用データを収集し、匿名化して、実際の展開シナリオを反映します。
+
+3. 合成生成：LLMを使用して初期例を生成し、専門家がそれを検証および洗練します。
+
+## ベストプラクティス
+
+- 評価方法論を徹底的に文書化し、仮定や制限を含める
+- ドメインのさまざまな側面をカバーする多様なテストケースを含める
+- 自動メトリックと人間の評価の両方を適用する
+- 評価データセットとコードをバージョン管理する
+- 新しいエッジケースや要件を発見するたびに評価スイートを定期的に更新する
+
+## 参考文献
+
+- [LightEvalカスタムタスクガイド](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [LightEvalカスタムメトリック](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- データセットアノテーションのための[Argillaドキュメント](https://docs.argilla.io)
+- 一般的な評価原則のための[評価ガイドブック](https://github.com/huggingface/evaluation-guidebook)
+
+# 次のステップ
+
+⏩ これらの概念を実装する完全な例については、[ドメイン評価プロジェクト](./project/README.md)を参照してください。
diff --git a/ja/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/ja/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
new file mode 100644
index 00000000..3e4f34a9
--- /dev/null
+++ b/ja/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z-6LLOPZouLg"
+   },
+   "source": [
+    "# LightEvalを使用してLLMを評価および分析する方法\n",
+    "\n",
+    "このノートブックでは、LightEvalを使用して大規模言語モデル（LLM）を評価および分析する方法を示します。LightEvalは、さまざまなタスクにわたってモデルの性能を評価するための柔軟で使いやすいフレームワークです。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fXqd9BXgouLi"
+   },
+   "source": [
+    "## 1. 開発環境のセットアップ\n",
+    "\n",
+    "最初のステップは、必要なライブラリをインストールすることです。`lighteval`、`transformers`、`datasets`を含みます。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tKvGVxImouLi"
+   },
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件のインストール\n",
+    "# !pip install lighteval transformers datasets\n",
+    "\n",
+    "# Hugging Faceへの認証\n",
+    "\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()\n",
+    "\n",
+    "# 便利のため、Hugging Faceのトークンを環境変数HF_TOKENとして設定できます"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XHUzfwpKouLk"
+   },
+   "source": [
+    "## 2. モデルとデータセットの読み込み"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "z4p6Bvo7ouLk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Downloading (…)okenizer_config.json: 100%|██████████| 28.0/28.0 [00:00<00:00, 28.0kB/s]\n",
+       "Downloading (…)lve/main/config.json: 100%|██████████| 1.11k/1.11k [00:00<00:00, 1.11MB/s]\n",
+       "Downloading (…)olve/main/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 1.06MB/s]\n",
+       "Downloading (…)olve/main/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 525kB/s]\n",
+       "Downloading (…)/main/tokenizer.json: 100%|██████████| 2.13M/2.13M [00:00<00:00, 2.13MB/s]\n",
+       "Downloading (…)/main/adapter_config.json: 100%|██████████| 472/472 [00:00<00:00, 472kB/s]\n",
+       "Downloading (…)/adapter_model.bin: 100%|██████████| 1.22G/1.22G [00:10<00:00, 120MB/s]\n",
+       "Downloading pytorch_model.bin: 100%|██████████| 1.22G/1.22G [00:10<00:00, 120MB/s]\n"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# モデルとデータセットの読み込み\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "\n",
+    "# サンプルデータセットの読み込み\n",
+    "dataset = load_dataset(\"lighteval\", \"mmlu\")\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TOhJdtsouLk"
+   },
+   "source": [
+    "## 3. LightEvalを使用した評価\n",
+    "\n",
+    "LightEvalを使用してモデルを評価するために、評価タスクを定義し、パイプラインを設定します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# LightEvalのタスクとパイプラインをインポート\n",
+    "from lighteval.tasks import Task, Pipeline\n",
+    "from lighteval.metrics import EvaluationTracker\n",
+    "\n",
+    "# 評価するタスクを定義\n",
+    "tasks = [\n",
+    "    \"mmlu|anatomy|0|0\",\n",
+    "    \"mmlu|high_school_biology|0|0\",\n",
+    "    \"mmlu|high_school_chemistry|0|0\",\n",
+    "    \"mmlu|professional_medicine|0|0\"\n",
+    "]\n",
+    "\n",
+    "# パイプラインパラメータを設定\n",
+    "pipeline_params = {\n",
+    "    \"max_samples\": 40,  # 評価するサンプル数\n",
+    "    \"batch_size\": 1,    # 推論のバッチサイズ\n",
+    "    \"num_workers\": 4    # ワーカープロセスの数\n",
+    "}\n",
+    "\n",
+    "# 評価トラッカーを作成\n",
+    "evaluation_tracker = EvaluationTracker(\n",
+    "    output_path=\"./results\",\n",
+    "    save_generations=True\n",
+    ")\n",
+    "\n",
+    "# パイプラインを作成\n",
+    "pipeline = Pipeline(\n",
+    "    tasks=tasks,\n",
+    "    pipeline_parameters=pipeline_params,\n",
+    "    evaluation_tracker=evaluation_tracker,\n",
+    "    model=model\n",
+    ")\n",
+    "\n",
+    "# 評価を実行\n",
+    "pipeline.evaluate()\n",
+    "\n",
+    "# 結果を取得して表示\n",
+    "results = pipeline.get_results()\n",
+    "pipeline.show_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yO6E9quouLl"
+   },
+   "source": [
+    "## 4. 結果の分析\n",
+    "\n",
+    "評価結果を分析し、モデルの性能を理解します。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pandasを使用して結果を分析\n",
+    "import pandas as pd\n",
+    "\n",
+    "# 結果をDataFrameに変換\n",
+    "df = pd.DataFrame(results)\n",
+    "\n",
+    "# 結果を表示\n",
+    "print(df)\n",
+    "\n",
+    "# 結果を視覚化\n",
+    "df.plot(kind=\"bar\", x=\"Task\", y=\"Value\", legend=False)\n",
+    "plt.ylabel(\"Accuracy\")\n",
+    "plt.title(\"Model Performance on Different Tasks\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ja/4_evaluation/project/README.md b/ja/4_evaluation/project/README.md
new file mode 100644
index 00000000..ad6ec93f
--- /dev/null
+++ b/ja/4_evaluation/project/README.md
@@ -0,0 +1,83 @@
+# Argilla、Distilabel、LightEvalを使用したドメイン固有の評価
+
+ほとんどの人気のあるベンチマークは、一般的な能力（推論、数学、コード）を評価しますが、より具体的な能力を評価する必要がある場合はどうすればよいでしょうか？
+
+カスタムドメインに関連するモ���ルを評価する必要がある場合はどうすればよいでしょうか？（例えば、金融、法務、医療のユースケース）
+
+このチュートリアルでは、[Argilla](https://github.com/argilla-io/argilla)、[distilabel](https://github.com/argilla-io/distilabel)、および[LightEval](https://github.com/huggingface/lighteval)を使用して、関連するデータを作成し、サンプルに注釈を付け、モデルを評価するための完全なパイプラインを示します。例として、複数のドキュメントから試験問題を生成することに焦点を当てます。
+
+## プロジェクト構造
+
+このプロセスでは、データセットの生成、注釈の追加、評価のためのサンプルの抽出、実際のモデル評価の4つのステップを実行します。各ステップにはスクリプトがあります。
+
+| スクリプト名 | 説明 |
+|-------------|-------------|
+| generate_dataset.py | 指定された言語モデルを使用して複数のテキストドキュメントから試験問題を生成します。 |
+| annotate_dataset.py | 生成された試験問題の手動注釈のためのArgillaデータセットを作成します。 |
+| create_dataset.py | Argillaから注釈付きデータを処理し、Hugging Faceデータセットを作成します。 |
+| evaluation_task.py | 試験問題データセットの評価のためのカスタムLightEvalタスクを定義します。 |
+
+## ステップ
+
+### 1. データセットの生成
+
+`generate_dataset.py`スクリプトは、distilabelライブラリを使用して複数のテキストドキュメントに基づいて試験問題を生成します。指定されたモデル（デフォルト：Meta-Llama-3.1-8B-Instruct）を使用して質問、正しい回答、および誤った回答（ディストラクター）を作成します。独自のデータサンプルを追加し、異なるモデルを使用することもできます。
+
+生成を実行するには：
+
+```sh
+python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory
+```
+
+これにより、入力ディレクトリ内のすべてのドキュメントに対して生成された試験問題を含む[Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/)が作成されます。
+
+### 2. データセットの注釈
+
+`annotate_dataset.py`スクリプトは、生成された質問を取得し、注釈のためのArgillaデータセットを作成します。データセットの構造を設定し、生成された質問と回答をランダムな順序で入力します。これにより、バイアスを避けることができます。Argillaでは、正しい回答が提案として表示されます。
+
+LLMからの提案された正しい回答がランダムな順序で表示され、正しい回答を承認するか、別の回答を選択できます。このプロセスの所要時間は、評価データセットの規模、ドメインデータの複雑さ、およびLLMの品質によって異なります。例えば、Llama-3.1-70B-Instructを使用して、転移学習のドメインで150サンプルを1時間以内に作成できました。
+
+注釈プロセスを実行するには：
+
+```sh
+python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name
+```
+
+これにより、手動レビューと注釈のためのArgillaデータセットが作成されます。
+
+![argilla_dataset](./images/domain_eval_argilla_view.png)
+
+Argillaを使用していない場合は、この[クイックスタートガイド](https://docs.argilla.io/latest/getting_started/quickstart/)に従ってローカルまたはスペースにデプロイしてください。
+
+### 3. データセットの作成
+
+`create_dataset.py`スクリプトは、Argillaから注釈付きデータを処理し、Hugging Faceデータセットを作成します。提案された回答と手動で注釈された回答の両方を処理します。このスクリプトは、質問、可能な回答、および正しい回答の列名を含むデータセットを作成します。最終データセットを作成するには：
+
+```sh
+huggingface_hub login
+python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id
+```
+
+これにより、指定されたリポジトリにデータセットがHugging Face Hubにプッシュされます。サンプルデータセットは[こちら](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train)で確認できます。データセットのプレビューは次のようになります：
+
+![hf_dataset](./images/domain_eval_dataset_viewer.png)
+
+### 4. 評価タスク
+
+`evaluation_task.py`スクリプトは、試験問題データセットの評価のためのカスタムLightEvalタスクを定義します。プロンプト関数、カスタム精度メトリック、およびタスク構成が含まれます。
+
+カスタム試験問題タスクを使用してlightevalでモデルを評価するには：
+
+```sh
+lighteval accelerate \
+    --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
+    --tasks "community|exam_questions|0|0" \
+    --custom_tasks domain-eval/evaluation_task.py \
+    --output_dir "./evals"
+```
+
+詳細なガイドはlighteval wikiで確認できます：
+
+- [カスタムタスクの作成](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task)
+- [カスタムメトリックの作成](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)
+- [既存のメトリックの使用](https://github.com/huggingface/lighteval/wiki/Metric-List)
diff --git a/ja/4_evaluation/project/annotate_dataset.py b/ja/4_evaluation/project/annotate_dataset.py
new file mode 100644
index 00000000..f57a4fb6
--- /dev/null
+++ b/ja/4_evaluation/project/annotate_dataset.py
@@ -0,0 +1,129 @@
+import argparse
+import json
+from random import choices, sample
+
+import argilla as rg
+from distilabel.distiset import Distiset
+
+################################################################################
+# スクリプトのパラメータ
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="Argillaを使用して試験問題データセットに注釈を付けます。"
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="ArgillaのAPIキー",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="ArgillaのAPI URL",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="試験問題データセットのパス",
+)
+parser.add_argument(
+    "--dataset_config",
+    type=str,
+    default="default",
+    help="データセットの構成",
+)
+parser.add_argument(
+    "--dataset_split",
+    type=str,
+    default="train",
+    help="使用するデータセットの分割",
+)
+parser.add_argument(
+    "--output_dataset_name",
+    type=str,
+    default="exam_questions",
+    help="出力Argillaデータセットの名前",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# 検証のためのフィードバックタスクを使用してArgillaデータセットを作成
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+
+if client.datasets(args.output_dataset_name):
+    print(f"既存のデータセット '{args.output_dataset_name}' を削除します")
+    client.datasets(args.output_dataset_name).delete()
+
+settings = rg.Settings(
+    fields=[
+        rg.TextField("question"),
+        rg.TextField("answer_a"),
+        rg.TextField("answer_b"),
+        rg.TextField("answer_c"),
+        rg.TextField("answer_d"),
+    ],
+    questions=[
+        rg.LabelQuestion(
+            name="correct_answer",
+            labels=["answer_a", "answer_b", "answer_c", "answer_d"],
+        ),
+        rg.TextQuestion(
+            name="improved_question",
+            description="質問を改善できますか？",
+        ),
+        rg.TextQuestion(
+            name="improved_answer",
+            description="最良の回答を改善できますか？",
+        ),
+    ],
+)
+
+dataset = rg.Dataset(settings=settings, name=args.output_dataset_name)
+dataset.create()
+
+################################################################################
+# Distisetを読み込み、Argillaデータセットにレコードを処理して追加
+# バイアスを避けるために質問がランダムな順序で表示されることを確認します
+# ただし、Argilla UIでは正しい回答を提案として表示します。
+################################################################################
+
+distiset = Distiset.load_from_disk(args.dataset_path)
+answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"]
+dataset_records = []
+
+for exam in distiset[args.dataset_config][args.dataset_split]:
+    exam_json = json.loads(exam["generation"])["exam"]
+
+    for question in exam_json:
+        answer = question["answer"]
+        distractors = question["distractors"]
+        distractors = choices(distractors, k=3)
+        answers = distractors + [answer]
+        answers = sample(answers, len(answers))
+        suggestion_idx = answers.index(answer)
+        fields = dict(zip(answer_names, answers))
+        fields["question"] = question["question"]
+
+        record = rg.Record(
+            fields=fields,
+            suggestions=[
+                rg.Suggestion(
+                    question_name="correct_answer",
+                    value=answer_names[suggestion_idx],
+                )
+            ],
+        )
+        dataset_records.append(record)
+
+dataset.records.log(dataset_records)
+
+print(
+    f"データセット '{args.output_dataset_name}' がArgillaに作成され、入力されました。"
+)
diff --git a/ja/4_evaluation/project/create_dataset.py b/ja/4_evaluation/project/create_dataset.py
new file mode 100644
index 00000000..f3b5c812
--- /dev/null
+++ b/ja/4_evaluation/project/create_dataset.py
@@ -0,0 +1,72 @@
+import argparse
+
+import argilla as rg
+from datasets import Dataset
+
+################################################################################
+# スクリプトのパラメータ
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="注釈付きArgillaデータからHugging Faceデータセットを作成します。"
+)
+parser.add_argument(
+    "--argilla_api_key",
+    type=str,
+    default="argilla.apikey",
+    help="ArgillaのAPIキー",
+)
+parser.add_argument(
+    "--argilla_api_url",
+    type=str,
+    default="http://localhost:6900",
+    help="ArgillaのAPI URL",
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default="exam_questions",
+    help="Argillaデータセットのパス",
+)
+parser.add_argument(
+    "--dataset_repo_id",
+    type=str,
+    default="burtenshaw/exam_questions",
+    help="Hugging FaceデータセットリポジトリID",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# Argillaクライアントを初期化し、データセットを読み込む
+################################################################################
+
+client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url)
+dataset = client.datasets(args.dataset_path)
+
+################################################################################
+# Argillaレコードを処理
+################################################################################
+
+dataset_rows = []
+
+for record in dataset.records(with_suggestions=True, with_responses=True):
+    row = record.fields
+
+    if len(record.responses) == 0:
+        answer = record.suggestions["correct_answer"].value
+        row["correct_answer"] = answer
+    else:
+        for response in record.responses:
+            if response.question_name == "correct_answer":
+                row["correct_answer"] = response.value
+    dataset_rows.append(row)
+
+################################################################################
+# Hugging Faceデータセットを作成し、Hubにプッシュ
+################################################################################
+
+hf_dataset = Dataset.from_list(dataset_rows)
+hf_dataset.push_to_hub(repo_id=args.dataset_repo_id)
+
+print(f"データセットが{args.dataset_repo_id}に正常にプッシュされました")
diff --git a/ja/4_evaluation/project/evaluation_task.py b/ja/4_evaluation/project/evaluation_task.py
new file mode 100644
index 00000000..9c643c9f
--- /dev/null
+++ b/ja/4_evaluation/project/evaluation_task.py
@@ -0,0 +1,87 @@
+import numpy as np
+
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+from lighteval.metrics.utils.metric_utils import (
+    SampleLevelMetric,
+    MetricCategory,
+    MetricUseCase,
+)
+
+################################################################################
+# データセットの構造に基づいてプロンプト関数を定義
+################################################################################
+
+
+def prompt_fn(line, task_name: str = None):
+    """データセットの行を評価用のDocオブジェクトに変換します。"""
+    instruction = "次の試験問題に対して正しい答えを選んでください："
+    return Doc(
+        task_name=task_name,
+        query=f"{instruction} {line['question']}",
+        choices=[
+            f" {line['answer_a']}",
+            f" {line['answer_b']}",
+            f" {line['answer_c']}",
+            f" {line['answer_d']}",
+        ],
+        gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index(
+            line["correct_answer"]
+        ),
+        instruction=instruction,
+    )
+
+
+################################################################################
+# カスタムメトリックを定義
+# ガイドに基づいて https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric
+# または既存のメトリックを使用 https://github.com/huggingface/lighteval/wiki/Metric-List
+# 既存のメトリックは lighteval.metrics.metrics からインポート可能
+################################################################################
+
+
+def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool:
+    response = np.argmin(kwargs["choices_logprob"])
+    return response == formatted_doc.gold_index
+
+
+custom_metric = SampleLevelMetric(
+    metric_name="exam_question_accuracy",
+    higher_is_better=True,
+    category=MetricCategory.MULTICHOICE,
+    use_case=MetricUseCase.NONE,
+    sample_level_fn=sample_level_fn,
+    corpus_level_fn=np.mean,
+)
+
+################################################################################
+# プロンプト関数とカスタムメトリックに基づいてタスクを定義
+# ガイドに基づいて https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task
+################################################################################
+
+task = LightevalTaskConfig(
+    name="example",
+    prompt_function=prompt_fn,
+    suite=["community"],
+    hf_repo="burtenshaw/exam_questions",
+    hf_subset="default",
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    few_shots_split=None,
+    few_shots_select=None,
+    metric=[custom_metric],
+)
+
+# TASKS_TABLEにタスクを追加
+TASKS_TABLE = [task]
+
+# モジュールロジック
+if __name__ == "__main__":
+    print([t.name for t in TASKS_TABLE])
+    print(len(TASKS_TABLE))
+
+# lighteval accelerate \
+# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \
+# "community|example|0|0" \
+# --custom-tasks "submitted_tasks/example.py" \
+# --output-dir "results"
diff --git a/ja/4_evaluation/project/generate_dataset.py b/ja/4_evaluation/project/generate_dataset.py
new file mode 100644
index 00000000..3bd42bfd
--- /dev/null
+++ b/ja/4_evaluation/project/generate_dataset.py
@@ -0,0 +1,168 @@
+import argparse
+import os
+from pydantic import BaseModel, Field
+from datasets import Dataset
+from typing import List
+
+from distilabel.llms import InferenceEndpointsLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps.tasks import TextGeneration
+
+
+################################################################################
+# スクリプトのパラメータ
+################################################################################
+
+parser = argparse.ArgumentParser(
+    description="ディレクトリ内のテキストファイルから試験問題を生成します。"
+)
+parser.add_argument(
+    "--model_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="テキスト生成のためのモデルID",
+)
+parser.add_argument(
+    "--tokenizer_id",
+    type=str,
+    default="Qwen/Qwen2.5-7B-Instruct",
+    help="テキスト生成のためのトークナイザーID",
+)
+parser.add_argument(
+    "--input_dir",
+    type=str,
+    help="入力テキストファイルを含むディレクトリ",
+    default="data",
+)
+parser.add_argument(
+    "--max_new_tokens",
+    type=int,
+    default=2048,
+    help="生成する新しいトークンの最大数",
+)
+parser.add_argument(
+    "--output_path",
+    type=str,
+    default="exam_questions_output",
+    help="生成されたデータセットを保存するディレクトリ",
+)
+
+args = parser.parse_args()
+
+################################################################################
+# ドキュメントの読み込み
+# ドキュメントは入力ディレクトリにあり、各ファイルが同じトピックに関する
+# 個別のドキュメントであると仮定します。
+################################################################################
+
+# 入力ディレクトリ内のすべてのテキストファイルを処理
+documents = []
+for filename in os.listdir(args.input_dir):
+    if filename.endswith(".txt"):
+        file_path = os.path.join(args.input_dir, filename)
+        with open(file=file_path, mode="r", encoding="utf-8") as file:
+            document_content = file.read()
+            documents.append(document_content)
+
+# すべてのドキュメント内容から単一のデータセットを作成
+dataset = Dataset.from_dict({"document": documents})
+
+################################################################################
+# プロンプトの定義
+# モデルが正しい出力形式を生成するようにシステムプロンプトを使用します。
+# テンプレートを使用してドキュメントをプロンプトに挿入します。
+################################################################################
+
+SYSTEM_PROMPT = """\
+あなたは学生のための試験問題を作成する専門家です。
+提供されたドキュメントに基づいて質問と回答を作成し、
+質問に対する正しい回答と、誤っているが妥当な回答のリストを作成してください。
+回答は次の形式に従う必要があります：
+```
+[
+    {
+        "question": "質問内容",
+        "answer": "質問に対する正しい回答",
+        "distractors": ["誤った回答1", "誤った回答2", "誤った回答3"]
+    },
+    ... (必要に応じてさらに質問と回答を追加)
+]
+```
+""".strip()
+
+INSTRUCTION_TEMPLATE = """\
+    ドキュメントに関する質問と回答のリストを生成してください。 
+    ドキュメント:\n\n{{ instruction }}"""
+
+################################################################################
+# 出力構造の定義
+# パイプラインの出力のデータモデルを定義し、評価タスクの正しい形式で
+# 出力があることを確認します。
+################################################################################
+
+
+class ExamQuestion(BaseModel):
+    question: str = Field(..., description="回答すべき質問")
+    answer: str = Field(..., description="質問に対する正しい回答")
+    distractors: List[str] = Field(
+        ..., description="質問に対する誤っているが妥当な回答のリスト"
+    )
+
+
+class ExamQuestions(BaseModel):
+    exam: List[ExamQuestion]
+
+
+################################################################################
+# パイプラインの作成
+# ドキュメントに基づいて試験問題を生成し、正しい形式で出力する単一のタスクを
+# 持つパイプラインを作成します。Hugging Face InferenceEndpointsと
+# 引数で指定されたモデルを使用します。
+################################################################################
+
+with Pipeline(
+    name="Domain-Eval-Questions",
+    description="提供されたドキュメントに基づいて試験問題を生成します。",
+) as pipeline:
+    # テキスト生成タスクの設定
+    text_generation = TextGeneration(
+        name="exam_generation",
+        llm=InferenceEndpointsLLM(
+            model_id=args.model_id,
+            tokenizer_id=args.model_id,
+            api_key=os.environ["HF_TOKEN"],
+            structured_output={
+                "schema": ExamQuestions.model_json_schema(),
+                "format": "json",
+            },
+        ),
+        input_batch_size=8,
+        output_mappings={"model_name": "generation_model"},
+        input_mappings={"instruction": "document"},
+        system_prompt=SYSTEM_PROMPT,
+        template=INSTRUCTION_TEMPLATE,
+    )
+
+
+################################################################################
+# パイプラインの実行
+# すべてのドキュメントに対してパイプラインを実行し、結果を出力パスに保存します。
+################################################################################
+
+if __name__ == "__main__":
+    # すべてのドキュメントに対してパイプラインを実行
+    distiset = pipeline.run(
+        parameters={
+            "exam_generation": {
+                "llm": {
+                    "generation_kwargs": {
+                        "max_new_tokens": args.max_new_tokens,
+                    }
+                }
+            }
+        },
+        use_cache=False,
+        dataset=dataset,
+    )
+
+    distiset.save_to_disk(args.output_path)
diff --git a/ja/README.md b/ja/README.md
new file mode 100644
index 00000000..32d11ea2
--- /dev/null
+++ b/ja/README.md
@@ -0,0 +1,90 @@
+![smolcourse image](./banner.png)
+
+# スモールコース
+
+これは、特定のユースケースに合わせて言語モデルを調整するための実践的なコースです。ほとんどのローカルマシンで実行できるため、言語モデルの調整を始めるのに便利です。GPUの要件は最小限で、有料サービスは必要ありません。このコースは[SmolLM2](https://github.com/huggingface/smollm/tree/main)シリーズのモデルに基づいていますが、ここで学んだスキルを大規模なモデルや他の小型言語モデルに転用することができます。
+
+<a href="http://hf.co/join/discord">
+<img src="https://img.shields.io/badge/Discord-7289DA?&logo=discord&logoColor=white"/>
+</a>
+
+<div style="background: linear-gradient(to right, #e0f7fa, #e1bee7, orange); padding: 20px; border-radius: 5px; margin-bottom: 20px; color: purple;">
+    <h2>参加は無料で、今すぐ始められます！</h2>
+    <p>このコースはオープンでピアレビューされています。コースに参加するには、<strong>プルリクエストを開く</strong>ことで、あなたの作業をレビューに提出してください。以下の手順に従ってください：</p>
+    <ol>
+        <li>リポジトリをフォークします <a href="https://github.com/huggingface/smol-course/fork">こちら</a></li>
+        <li>資料を読み、変更を加え、演習を行い、自分の例を追加します。</li>
+        <li>december_2024ブランチでプルリクエストを開きます</li>
+        <li>レビューを受けてマージされます</li>
+    </ol>
+    <p>これにより、学習を助け、常に改善されるコミュニティ主導のコースを構築することができます。</p>
+</div>
+
+このプロセスについては、この[ディスカッションスレッド](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932)で議論できます。
+
+## コース概要
+
+このコースは、小型言語モデルを使用した実践的なアプローチを提供し、初期のトレーニングから本番展開までをカバーします。
+
+| モジュール | 説明 | ステータス | リリース日 |
+|--------|-------------|---------|--------------|
+| [インストラクションチューニング](./1_instruction_tuning) | 教師あり微調整、チャットテンプレート、および基本的な指示に従う方法を学びます | ✅ 準備完了 | 2024年12月3日 |
+| [選好整合](./2_preference_alignment) | DPOおよびORPO技術を探求し、人間の選好にモデルを整合させる方法を学びます | ✅ 準備完了  | 2024年12月6日 |
+| [パラメータ効率の良い微調整](./3_parameter_efficient_finetuning) | LoRA、プロンプトチューニング、および効率的な適応方法を学びます | ✅ 準備完了 | 2024年12月9日 |
+| [評価](./4_evaluation) | 自動ベンチマークを使用し、カスタムドメイン評価を作成する方法を学びます | ✅ 準備完了 | 2024年12月13日 |
+| [ビジョン言語モデル](./5_vision_language_models) | マルチモーダルモデルをビジョン言語タスクに適応させる方法を学びます | ✅ 準備完了 | 2024年12月16日 |
+| [合成データセット](./6_synthetic_datasets) | トレーニング用の合成データセットを作成し、検証する方法を学びます | ✅ 準備完了 | 2024年12月20日 |
+| [推論](./7_inference) | モデルを効率的に推論する方法を学びます | [🚧 作業中](https://github.com/huggingface/smol-course/pull/150) | 2025年1月8日 |
+| [エージェント](./8_agents) | 自分のエージェントAIを構築する方法を学びます | ✅ 準備完了 | 2025年1月13日 ||
+| キャップストーンプロジェクト | 学んだことを使ってリーダーボードを登りましょう！ | [🚧 作業中](https://github.com/huggingface/smol-course/pull/97) | 2025年1月10日 |
+
+## なぜ小型言語モデルなのか？
+
+大規模な言語モデルは印象的な能力を示していますが、しばしば多くの計算リソースを必要とし、特定のアプリケーションには過剰な場合があります。小型言語モデルは、ドメイン固有のアプリケーションに対していくつかの利点を提供します：
+
+- **効率性**：トレーニングと展開に必要な計算リソースが大幅に少ない
+- **カスタマイズ**：特定のドメインに簡単に微調整および適応可能
+- **制御**：モデルの動作をよりよく理解し、制御できる
+- **コスト**：トレーニングと推論の運用コストが低い
+- **プライバシー**：データを外部APIに送信せずにローカルで実行可能
+- **グリーンテクノロジー**：リソースの効率的な使用を推進し、炭素排出量を削減
+- **学術研究の容易さ**：最先端のLLMを使用した学術研究のための簡単なスターターを提供し、物流の制約を減らす
+
+## 前提条件
+
+開始する前に、以下を確認してください：
+- 機械学習と自然言語処理の基本的な理解
+- Python、PyTorch、および`transformers`ライブラリに精通していること
+- 事前学習された言語モデルとラベル付きデータセットへのアクセス
+
+## インストール
+
+コースをパッケージとして維持しているため、パッケージマネージャーを使用して依存関係を簡単にインストールできます。`uv`をお勧めしますが、`pip`や`pdm`などの代替手段も使用できます。
+
+### `uv`を使用する場合
+
+`uv`がインストールされている場合、次のようにしてコースをインストールできます：
+
+```bash
+uv venv --python 3.11.0
+uv sync
+```
+
+### `pip`を使用する場合
+
+すべての例は**python 3.11**環境で実行されるため、次のように環境を作成し、依存関係をインストールします：
+
+```bash
+# python -m venv .venv
+# source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+### Google Colab
+
+**Google Colabから**は、使用するハードウェアに基づいて柔軟に依存関係をインストールする必要があります。次のようにします：
+
+```bash
+pip install transformers trl datasets huggingface_hub
+```
+

From 04f3583686bcc7a0cc977bb29b843f374e97b972 Mon Sep 17 00:00:00 2001
From: Ikko Ashimine <ashimine_ikko_bp@tenso.com>
Date: Fri, 17 Jan 2025 00:31:14 +0900
Subject: [PATCH 43/63] docs: update ja/README.md

---
 ja/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ja/README.md b/ja/README.md
index 32d11ea2..b7f0e6e8 100644
--- a/ja/README.md
+++ b/ja/README.md
@@ -1,4 +1,4 @@
-![smolcourse image](./banner.png)
+![smolcourse image](../banner.png)
 
 # スモールコース
 

From daed87f8cfcdc03ae046b852307d8a4d21866e9c Mon Sep 17 00:00:00 2001
From: Kwadwo Agyapon-Ntra <kayoghonline@gmail.com>
Date: Fri, 17 Jan 2025 18:39:58 +0000
Subject: [PATCH 44/63] Fixed minor typo in supervised_fine_tuning.md

---
 1_instruction_tuning/supervised_fine_tuning.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1_instruction_tuning/supervised_fine_tuning.md b/1_instruction_tuning/supervised_fine_tuning.md
index dc236962..8c6c3df4 100644
--- a/1_instruction_tuning/supervised_fine_tuning.md
+++ b/1_instruction_tuning/supervised_fine_tuning.md
@@ -28,7 +28,7 @@ SFT plays a fundamental role in aligning language models with human preferences.
 
 ## Supervised Fine-Tuning With Transformer Reinforcement Learning
 
-A key software package for Supervised Fine-Tuning is Transformer Reinforcement Learning (TRL). TRL is a toolkit used to train transformer language models models using reinforcement learning (RL).
+A key software package for Supervised Fine-Tuning is Transformer Reinforcement Learning (TRL). TRL is a toolkit used to train transformer language models using reinforcement learning (RL).
 
 Built on top of the Hugging Face Transformers library, TRL allows users to directly load pretrained language models and supports most decoder and encoder-decoder architectures. The library facilitates major processes of RL used in language modelling, including supervised fine-tuning (SFT), reward modeling (RM), proximal policy optimization (PPO), and Direct Preference Optimization (DPO). We will use TRL in a number of modules throughout this repo.
 

From 95063f20f999223c1742db5bb77de1dfcdb39fd4 Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Fri, 17 Jan 2025 21:03:52 +0100
Subject: [PATCH 45/63] respond to comments

---
 7_inference/inference_pipeline.md        | 27 ++++++++++--------------
 7_inference/text_generation_inference.md |  1 -
 2 files changed, 11 insertions(+), 17 deletions(-)

diff --git a/7_inference/inference_pipeline.md b/7_inference/inference_pipeline.md
index cff809bf..e7f5c2c7 100644
--- a/7_inference/inference_pipeline.md
+++ b/7_inference/inference_pipeline.md
@@ -71,6 +71,7 @@ generator = pipeline(
 ```
 
 ### Generation Parameters
+
 ```python
 response = generator(
     "Translate this to French:",
@@ -112,13 +113,14 @@ for prompt, response in zip(prompts, responses):
 
 ## Web Server Integration
 
-Here's how to integrate a pipeline into a Flask application:
+Here's how to integrate a pipeline into a FastAPI application:
 
 ```python
-from flask import Flask, request, jsonify
+from fastapi import FastAPI, HTTPException
 from transformers import pipeline
+import uvicorn
 
-app = Flask(__name__)
+app = FastAPI()
 
 # Initialize pipeline globally
 generator = pipeline(
@@ -127,17 +129,12 @@ generator = pipeline(
     device_map="auto"
 )
 
-@app.route("/generate", methods=["POST"])
-def generate_text():
+@app.post("/generate")
+async def generate_text(prompt: str):
     try:
-        data = request.json
-        prompt = data.get("prompt", "")
-        
-        # Input validation
         if not prompt:
-            return jsonify({"error": "No prompt provided"}), 400
+            raise HTTPException(status_code=400, detail="No prompt provided")
             
-        # Generate response with timeout
         response = generator(
             prompt,
             max_new_tokens=100,
@@ -145,15 +142,13 @@ def generate_text():
             temperature=0.7
         )
         
-        return jsonify({
-            "generated_text": response[0]['generated_text']
-        })
+        return {"generated_text": response[0]['generated_text']}
         
     except Exception as e:
-        return jsonify({"error": str(e)}), 500
+        raise HTTPException(status_code=500, detail=str(e))
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=5000)
+    uvicorn.run(app, host="0.0.0.0", port=5000)
 ```
 
 ## Limitations
diff --git a/7_inference/text_generation_inference.md b/7_inference/text_generation_inference.md
index 1552c366..9cf20622 100644
--- a/7_inference/text_generation_inference.md
+++ b/7_inference/text_generation_inference.md
@@ -10,7 +10,6 @@ Text Generation Inference addresses the key challenges of deploying large langua
 - **Continuous Batching**: The continuous batching system maximizes GPU utilization by dynamically processing requests, while optimizations like Flash Attention and Paged Attention significantly reduce memory usage and increase speed.
 - **Token Streaming**: Real-time applications benefit from token streaming via Server-Sent Events, delivering responses with minimal latency.
 
-
 ## How to Use Text Generation Inference
 
 ### Basic Python Usage

From e94e9479c000c194b1ccd02ef32ef0bba29b882c Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Fri, 17 Jan 2025 21:05:41 +0100
Subject: [PATCH 46/63] Set inference chapter to ready

---
 README.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 76152a0d..acb0cc20 100644
--- a/README.md
+++ b/README.md
@@ -34,9 +34,8 @@ This course provides a practical, hands-on approach to working with small langua
 | [Evaluation](./4_evaluation) | Use automatic benchmarks and create custom domain evaluations | ✅ Ready | Dec 13, 2024 |
 | [Vision-language Models](./5_vision_language_models) | Adapt multimodal models for vision-language tasks | ✅ Ready | Dec 16, 2024 |
 | [Synthetic Datasets](./6_synthetic_datasets) | Create and validate synthetic datasets for training | ✅ Ready | Dec 20, 2024 |
-| [Inference](./7_inference) | Infer with models efficiently | [🚧 WIP](https://github.com/huggingface/smol-course/pull/150) | Jan 8, 2025 |
+| [Inference](./7_inference) | Infer with models efficiently | ✅ Ready | Jan 8, 2025 |
 | [Agents](./8_agents) | Build your own agentic AI | ✅ Ready | Jan 13, 2025 ||
-| Capstone Project | Use what you've learnt to climb the leaderboard! | [🚧 WIP](https://github.com/huggingface/smol-course/pull/97) | Jan 10, 2025 |
 
 ## Why Small Language Models?
 

From 8d2229a5409fd652513a5de80a4d975fe1f51b37 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Fri, 17 Jan 2025 21:40:33 +0100
Subject: [PATCH 47/63] init

---
 vi/5_vision_language_models/README.md         |  41 ++
 .../images/VLM_Architecture.png               | Bin 0 -> 62242 bytes
 .../images/VLM_Process.png                    | Bin 0 -> 848749 bytes
 .../images/VLM_Usage.png                      | Bin 0 -> 1351937 bytes
 .../notebooks/vlm_sft_sample.ipynb            | 461 +++++++++++++++++
 .../notebooks/vlm_usage_sample.ipynb          | 469 ++++++++++++++++++
 vi/5_vision_language_models/vlm_finetuning.md | 102 ++++
 vi/5_vision_language_models/vlm_usage.md      |  76 +++
 vi/6_synthetic_datasets/README.md             |  39 ++
 vi/6_synthetic_datasets/images/pipeline.png   | Bin 0 -> 24977 bytes
 .../images/taxonomy-synthetic-data.png        | Bin 0 -> 281393 bytes
 .../instruction_datasets.md                   | 204 ++++++++
 .../notebooks/instruction_sft_dataset.ipynb   | 119 +++++
 .../notebooks/preference_dpo_dataset.ipynb    | 120 +++++
 .../preference_datasets.md                    | 155 ++++++
 15 files changed, 1786 insertions(+)
 create mode 100644 vi/5_vision_language_models/README.md
 create mode 100644 vi/5_vision_language_models/images/VLM_Architecture.png
 create mode 100644 vi/5_vision_language_models/images/VLM_Process.png
 create mode 100644 vi/5_vision_language_models/images/VLM_Usage.png
 create mode 100644 vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
 create mode 100644 vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
 create mode 100644 vi/5_vision_language_models/vlm_finetuning.md
 create mode 100644 vi/5_vision_language_models/vlm_usage.md
 create mode 100644 vi/6_synthetic_datasets/README.md
 create mode 100644 vi/6_synthetic_datasets/images/pipeline.png
 create mode 100644 vi/6_synthetic_datasets/images/taxonomy-synthetic-data.png
 create mode 100644 vi/6_synthetic_datasets/instruction_datasets.md
 create mode 100644 vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
 create mode 100644 vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
 create mode 100644 vi/6_synthetic_datasets/preference_datasets.md

diff --git a/vi/5_vision_language_models/README.md b/vi/5_vision_language_models/README.md
new file mode 100644
index 00000000..2f04c1f6
--- /dev/null
+++ b/vi/5_vision_language_models/README.md
@@ -0,0 +1,41 @@
+# Mô hình Ngôn ngữ Thị giác
+
+## 1. Sử dụng Mô hình Ngôn ngữ Thị giác (Vision Language Models)
+
+Mô hình Ngôn ngữ Thị giác (VLMs) xử lý đầu vào hình ảnh cùng với văn bản để thực hiện các tác vụ như chú thích của ảnh, trả lời câu hỏi bằng hình ảnh và suy luận đa phương thức (multimodal).
+
+Một kiến trúc VLM điển hình bao gồm:
+1. Bộ mã hóa hình ảnh (*image encoder*) để trích xuất các đặc trưng thị giác
+2. Lớp chiếu (*projection layer*) để căn chỉnh các biểu diễn thị giác với văn bản
+3. Mô hình ngôn ngữ để xử lý hoặc tạo văn bản. Điều này cho phép mô hình thiết lập các kết nối giữa các yếu tố về thị giác và các khái niệm trong ngôn ngữ.
+
+Tùy thuộc vào từng trường hợp mà có thể sử dụng các VLMs được huấn luyện theo các tác vụ khác nhau. Các mô hình cơ sở (base models) xử lý các tác vụ thị giác-ngôn ngữ tổng quát, trong khi các biến thể tối ưu hóa cho trò chuyện (chat-optimized variants) hỗ trợ các tương tác hội thoại. Một số mô hình bao gồm các thành phần bổ sung để làm rõ dự đoán dựa trên các bằng chứng thị giác (*visual evidence*) hoặc chuyên về các tác vụ cụ thể như phát hiện đối tượng (*object detection*).
+
+Để biết thêm chi tiết về kỹ thuật và cách sử dụng VLMs, hãy tham khảo trang [Sử dụng VLM](./vlm_usage.md).
+
+## 2. Tinh chỉnh Mô hình Ngôn ngữ Thị giác (VLM)
+
+Tinh chỉnh VLM là việc điều chỉnh một mô hình đã được huấn luyện trước (*pre-trained*) để thực hiện các tác vụ cụ thể hoặc để hoạt động hiệu quả trên một tập dữ liệu cụ thể. Quá trình này có thể tuân theo các phương pháp như tinh chỉnh có giám sát (*supervised fine-tuning*), tối ưu hóa tùy chọn (*preference optimization*) hoặc phương pháp kết hợp (*hybrid approach*) cả hai, như đã giới thiệu trong Chương 1 và Chương 2.
+
+Mặc dù các công cụ và kỹ thuật cốt lõi vẫn tương tự như các công cụ và kỹ thuật được sử dụng cho các mô hình ngôn ngữ (LLMs), việc tinh chỉnh VLMs đòi hỏi phải tập trung nhiều hơn vào việc biểu diễn và chuẩn bị dữ liệu cho hình ảnh. Điều này đảm bảo mô hình tích hợp và xử lý hiệu quả cả dữ liệu thị giác và văn bản để đạt hiệu suất tối ưu. Vì mô hình demo, SmolVLM, lớn hơn đáng kể so với mô hình ngôn ngữ được sử dụng trong bài trước, điều cần thiết là phải khám phá các phương pháp tinh chỉnh hiệu quả. Các kỹ thuật như lượng tử hóa (*quantization*) và Tinh chỉnh hiệu quả tham số - PEFT (*Parameter-Efficient Fine-Tuning*) có thể giúp làm cho quá trình này dễ tiếp cận hơn và tiết kiệm chi phí hơn, cho phép nhiều người dùng thử nghiệm với mô hình hơn.
+
+Để được hướng dẫn chi tiết về tinh chỉnh VLMs, hãy truy cập trang [Tinh chỉnh VLM](./vlm_finetuning.md).
+
+## Bài tập thực hành
+
+| Tiêu đề | Mô tả | Bài tập | Link | Colab |
+|-------|-------------|----------|------|-------|
+| Sử dụng VLM | Tìm hiểu cách tải và sử dụng VLM đã được huấn luyện trước cho các tác vụ khác nhau | 🐢 Xử lý một hình ảnh<br>🐕 Xử lý nhiều hình ảnh với xử lý hàng loạt <br>🦁 Xử lý toàn bộ video| [Notebook](./notebooks/vlm_usage_sample.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_usage_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| Tinh chỉnh VLM | Tìm hiểu cách tinh chỉnh VLM đã được huấn luyện trước cho các tập dữ liệu theo từng nhiệm vụ | 🐢 Sử dụng tập dữ liệu cơ bản để tinh chỉnh<br>🐕 Thử tập dữ liệu mới<br>🦁 Thử nghiệm với các phương pháp tinh chỉnh thay thế | [Notebook](./notebooks/vlm_sft_sample.ipynb)| <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_sft_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## Tài liệu tham khảo
+
+- [Hugging Face Learn: Tinh chỉnh có giám sát VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
+- [Hugging Face Learn: Tinh chỉnh có giám sát SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)
+- [Hugging Face Learn: Tinh chỉnh tối ưu hóa tùy chọn SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)
+- [Hugging Face Blog: Tối ưu hóa tùy chọn cho VLMs](https://huggingface.co/blog/dpo_vlm)
+- [Hugging Face Blog: Mô hình Ngôn ngữ Thị giác](https://huggingface.co/blog/vlms)
+- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)
+- [Hugging Face Model: SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct)
+- [CLIP: Learning Transferable Visual Models from Natural Language Supervision](https://arxiv.org/abs/2103.00020)
+- [Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://arxiv.org/abs/2107.07651)
diff --git a/vi/5_vision_language_models/images/VLM_Architecture.png b/vi/5_vision_language_models/images/VLM_Architecture.png
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diff --git a/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
new file mode 100644
index 00000000..5af7d6a2
--- /dev/null
+++ b/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
@@ -0,0 +1,461 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-Tuning a VLM \n",
+    "\n",
+    "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolVLM-Instruct` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercise: Fine-Tuning SmolVLM with SFTTrainer</h2>\n",
+    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
+    "    <p><b>Difficulty Levels</b></p>\n",
+    "    <p>🐢 Use the `HuggingFaceM4/ChartQA` dataset for SFT training run.</p>\n",
+    "    <p>🐕 Use the fine-tuned to model generate a response, and improve upon the base example.</p>\n",
+    "    <p>🦁 Try out the other datasets and show improvement.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "from huggingface_hub import login\n",
+    "login()\n",
+    "\n",
+    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import dependencies\n",
+    "\n",
+    "import torch\n",
+    "import os\n",
+    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
+    "from transformers.image_utils import load_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
+      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
+      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "quantization_config = BitsAndBytesConfig(load_in_4bit=True, \n",
+    "                                         bnb_4bit_use_double_quant=True, \n",
+    "                                         bnb_4bit_quant_type=\"nf4\", \n",
+    "                                         bnb_4bit_compute_dtype=torch.bfloat16)\n",
+    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
+    "model = AutoModelForVision2Seq.from_pretrained(\n",
+    "    model_name,\n",
+    "    quantization_config=quantization_config,\n",
+    "    torch_dtype=torch.bfloat16,\n",
+    ").to(device)\n",
+    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
+    "\n",
+    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "finetune_name = \"SmolVLM-FT-MyDataset\"\n",
+    "finetune_tags = [\"smol-course\", \"module_5\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset Preparation\n",
+    "\n",
+    "We will load a sample dataset and format it for training. The dataset should be structured with input-output pairs, where each input is a prompt and the output is the expected response from the model.\n",
+    "\n",
+    "**TRL will format input messages based on the model's chat templates.** They need to be represented as a list of dictionaries with the keys: `role` and `content`,."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
+       "        num_rows: 28299\n",
+       "    })\n",
+       "    val: Dataset({\n",
+       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
+       "        num_rows: 1920\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
+       "        num_rows: 2500\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load a sample dataset\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# TODO: define your dataset and config using the path and name parameters\n",
+    "dataset_name = \"HuggingFaceM4/ChartQA\"\n",
+    "ds = load_dataset(path=dataset_name)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=314x281 at 0x7D354DDE73D0>, 'query': 'What is the sum favourable  value in the year 2014 and 2015?', 'label': ['95'], 'human_or_machine': 0, 'image_size': ((314, 281),)}\n",
+      "Query: What is the sum favourable  value in the year 2014 and 2015?\n",
+      "Expected Answer: 95\n",
+      "Model Prediction: User:<image>What is the sum favourable  value in the year 2014 and 2015?\n",
+      "Answer: 91.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "sample = ds['train'][3]\n",
+    "\n",
+    "# Display the structure of a single sample\n",
+    "sample['image_size'] = sample['image'].size,\n",
+    "\n",
+    "# Visualize the image and related metadata\n",
+    "plt.imshow(sample['image'])\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Sample Chart Image\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(sample)\n",
+    "\n",
+    "# Preprocess the sample\n",
+    "prompt = [{\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": sample[\"query\"]}]}]\n",
+    "formatted_query = processor.apply_chat_template(prompt, tokenize=False)\n",
+    "\n",
+    "inputs = processor(\n",
+    "    images=sample[\"image\"], \n",
+    "    text=formatted_query, \n",
+    "    return_tensors=\"pt\"\n",
+    ").to(device)\n",
+    "inputs = {key: val.to(device, dtype=torch.bfloat16) if val.dtype == torch.float else val.to(device) for key, val in inputs.items()}\n",
+    "\n",
+    "# Generate predictions\n",
+    "with torch.no_grad():\n",
+    "    outputs = model.generate(**inputs,\n",
+    "                             max_length=1600)\n",
+    "\n",
+    "# Decode the prediction\n",
+    "prediction = processor.batch_decode(outputs, skip_special_tokens=True)\n",
+    "\n",
+    "# Display the result\n",
+    "print(f\"Query: {sample['query']}\")\n",
+    "print(f\"Expected Answer: {sample['label'][0]}\")\n",
+    "print(f\"Model Prediction: {prediction[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set Up LoRA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trainable params: 2,568,192 || all params: 2,248,841,072 || trainable%: 0.1142\n"
+     ]
+    }
+   ],
+   "source": [
+    "from peft import LoraConfig, get_peft_model\n",
+    "\n",
+    "# Configure LoRA\n",
+    "peft_config = LoraConfig(\n",
+    "    lora_alpha=16,\n",
+    "    lora_dropout=0.05,\n",
+    "    r=8,\n",
+    "    bias=\"none\",\n",
+    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
+    "    task_type=\"CAUSAL_LM\",\n",
+    ")\n",
+    "\n",
+    "# Apply PEFT model adaptation\n",
+    "peft_model = get_peft_model(model, peft_config)\n",
+    "\n",
+    "# Print trainable parameters\n",
+    "peft_model.print_trainable_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuring the Trainer\n",
+    "\n",
+    "The `Trainer` is configured with various parameters that control the training process. These include the number of training steps, batch size, learning rate, and evaluation strategy. Adjust these parameters based on your specific requirements and computational resources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def collate_fn(examples):\n",
+    "    # System message template for the VLM\n",
+    "    system_message = \"\"\"You are a Vision Language Model specialized in interpreting visual data from chart images.\n",
+    "    Your task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\n",
+    "    The charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\n",
+    "    Focus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.\"\"\"\n",
+    "\n",
+    "    # Initialize lists for text and image inputs\n",
+    "    text_inputs = []\n",
+    "    image_inputs = []\n",
+    "\n",
+    "    # Process all examples in one loop\n",
+    "    for example in examples:\n",
+    "        # Format the chat structure for the processor\n",
+    "        formatted_example = {\n",
+    "            \"messages\": [\n",
+    "                {\n",
+    "                    \"role\": \"system\",\n",
+    "                    \"content\": [{\"type\": \"text\", \"text\": system_message}],\n",
+    "                },\n",
+    "                {\n",
+    "                    \"role\": \"user\",\n",
+    "                    \"content\": [\n",
+    "                        {\n",
+    "                            \"type\": \"image\",\n",
+    "                        },\n",
+    "                        {\n",
+    "                            \"type\": \"text\",\n",
+    "                            \"text\": example[\"query\"],\n",
+    "                        },\n",
+    "                    ],\n",
+    "                },\n",
+    "            ]\n",
+    "        }\n",
+    "        # Apply chat template and strip extra spaces\n",
+    "        text_inputs.append(processor.apply_chat_template(formatted_example[\"messages\"], tokenize=False).strip())\n",
+    "        \n",
+    "        # Ensure images are in RGB mode\n",
+    "        image = example[\"image\"]\n",
+    "        if image.mode != 'RGB':\n",
+    "            image = image.convert('RGB')\n",
+    "        image_inputs.append( [image] )\n",
+    "\n",
+    "    # Tokenize the texts and process the images\n",
+    "    batch = processor(\n",
+    "        text=text_inputs,\n",
+    "        images=image_inputs,\n",
+    "        return_tensors=\"pt\",\n",
+    "        padding=True\n",
+    "    )\n",
+    "\n",
+    "    # Clone input IDs for labels\n",
+    "    labels = batch[\"input_ids\"].clone()\n",
+    "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Mask padding tokens in labels\n",
+    "\n",
+    "    # Ensure image_token is converted to string if it is an AddedToken\n",
+    "    # In some processor, processor.image_token return a list for each image.\n",
+    "    # TODO: AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\") only have one ?\n",
+    "    image_token_id = processor.tokenizer.convert_tokens_to_ids(str(processor.image_token))\n",
+    "\n",
+    "    # Mask image token IDs in the labels\n",
+    "    labels[labels == image_token_id] = -100\n",
+    "\n",
+    "    # Add labels back to the batch\n",
+    "    batch[\"labels\"] = labels\n",
+    "\n",
+    "\n",
+    "    return batch\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trl import SFTConfig, SFTTrainer\n",
+    "\n",
+    "# Configure the Trainer\n",
+    "training_args = SFTConfig(\n",
+    "    output_dir=\"sft_output\",  # Directory to save the model\n",
+    "    num_train_epochs=3,                     # number of training epochs\n",
+    "    per_device_train_batch_size=1,          # batch size per device during training\n",
+    "    gradient_accumulation_steps=16,         # number of steps before performing a backward/update pass\n",
+    "    gradient_checkpointing=True,            # use gradient checkpointing to save memory\n",
+    "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
+    "    logging_steps=5,                        # log every 10 steps\n",
+    "    save_strategy=\"epoch\",                  # save checkpoint every epoch\n",
+    "    learning_rate=2e-4,                     # learning rate, based on QLoRA paper\n",
+    "    bf16=True,                              # use bfloat16 precision\n",
+    "    tf32=True,                              # use tf32 precision\n",
+    "    max_grad_norm=0.3,                      # max gradient norm based on QLoRA paper\n",
+    "    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper\n",
+    "    lr_scheduler_type=\"constant\",           # use constant learning rate scheduler\n",
+    "    push_to_hub=True,                       # push model to hub\n",
+    "    gradient_checkpointing_kwargs = {\"use_reentrant\": False}, # use reentrant checkpointing\n",
+    "    # dataloader_num_workers=16, \n",
+    "    dataset_text_field=\"\", # need a dummy field for collator\n",
+    "    dataset_kwargs = {\"skip_prepare_dataset\": True}, # important for collator\n",
+    "    remove_unused_columns = False                    # necessary else features except label will be removed\n",
+    ")\n",
+    "# Initialize the Trainer\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=ds[\"train\"],\n",
+    "    eval_dataset=ds[\"test\"],\n",
+    "    data_collator=collate_fn,\n",
+    "    peft_config=peft_config,\n",
+    "    tokenizer=processor.tokenizer,\n",
+    ")\n",
+    "\n",
+    "# TODO: 🦁 🐕 align the SFTTrainer params with your chosen dataset. For example, if you are using the `bigcode/the-stack-smol` dataset, you will need to choose the `content` column`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "\n",
+    "With the trainer configured, we can now proceed to train the model. The training process will involve iterating over the dataset, computing the loss, and updating the model's parameters to minimize this loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b4e2897ddbc47fa90254bcf12ce92f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5304 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "trainer.train()\n",
+    "\n",
+    "# Save the model\n",
+    "trainer.save_model(f\"./{finetune_name}\")\n",
+    "\n",
+    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
+    "if os.getenv(\"HF_TOKEN\"):\n",
+    "    trainer.push_to_hub(tags=finetune_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 You're done!\n",
+    "\n",
+    "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolVLM` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n",
+    "\n",
+    "- Try this notebook on a harder difficulty\n",
+    "- Review a colleagues PR\n",
+    "- Improve the course material via an Issue or PR."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
new file mode 100644
index 00000000..59f29d42
--- /dev/null
+++ b/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Processing images and text with VLMs \n",
+    "\n",
+    "This notebook demonstrates how to utilize the `HuggingFaceTB/SmolVLM-Instruct` 4bit-quantized model for various multimodal tasks such as:\n",
+    "- Visual Question Answering (VQA): Answering questions based on image content.\n",
+    "- Text Recognition (OCR): Extracting and interpreting text in images.\n",
+    "- Video Understanding: Describing videos through sequential frame analysis.\n",
+    "\n",
+    "By structuring prompts effectively, you can leverage the model for many applications, such as scene understanding, document analysis, and dynamic visual reasoning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the requirements in Google Colab\n",
+    "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
+    "\n",
+    "# Authenticate to Hugging Face\n",
+    "from huggingface_hub import notebook_login\n",
+    "notebook_login()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
+      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
+      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'longest_edge': 1536}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch, PIL\n",
+    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
+    "from transformers.image_utils import load_image\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
+    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
+    "model = AutoModelForVision2Seq.from_pretrained(\n",
+    "    model_name,\n",
+    "    quantization_config=quantization_config,\n",
+    ").to(device)\n",
+    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
+    "\n",
+    "print(processor.image_processor.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Processing Images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with generating captions and answering questions about an image. We'll also explore processing multiple images.\n",
+    "### 1. Single Image Captioning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "image_url1 = \"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"\n",
+    "display(Image(url=image_url1))\n",
+    "\n",
+    "image_url2 = \"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"\n",
+    "display(Image(url=image_url2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>Can you describe the image?\\nAssistant: The image is a scene of a person walking in a forest. The person is wearing a coat and a cap. The person is holding the hand of another person. The person is walking on a path. The path is covered with dry leaves. The background of the image is a forest with trees.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load  one image\n",
+    "image1 = load_image(image_url1)\n",
+    "\n",
+    "# Create input messages\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"Can you describe the image?\"}\n",
+    "        ]\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Prepare inputs\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# Generate outputs\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Comparing Multiple Images\n",
+    "The model can process and compare multiple images. Let's determine the common theme between two images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Load images\n",
+    "image2 = load_image(image_url2)\n",
+    "\n",
+    "# Create input messages\n",
+    "messages = [\n",
+    "    # {\n",
+    "    #     \"role\": \"user\",\n",
+    "    #     \"content\": [\n",
+    "    #         {\"type\": \"image\"},\n",
+    "    #         {\"type\": \"image\"},\n",
+    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
+    "    #     ]\n",
+    "    # },\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
+    "        ]\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Prepare inputs\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# Generate outputs\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 🔠 Text Recognition (OCR)\n",
+    "VLM can also recognize and interpret text in images, making it suitable for tasks like document analysis.\n",
+    "You could try experimenting on images with denser text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
+     ]
+    }
+   ],
+   "source": [
+    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
+    "display(Image(url=document_image_url))\n",
+    "\n",
+    "# Load the document image\n",
+    "document_image = load_image(document_image_url)\n",
+    "\n",
+    "# Create input message for analysis\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# Prepare inputs\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# Generate outputs\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Processing videos\n",
+    "\n",
+    "Visual-Language Models (VLMs) can process videos indirectly by extracting keyframes and reasoning over them in temporal order. While VLMs lack the temporal modeling capabilities of dedicated video models, they can still:\n",
+    "- Describe actions or events by analyzing sampled frames sequentially.\n",
+    "- Answer questions about videos based on representative keyframes.\n",
+    "- Summarize video content by combining textual descriptions of multiple frames.\n",
+    "\n",
+    "Let experiment on an example:\n",
+    "\n",
+    "<video width=\"640\" height=\"360\" controls>\n",
+    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
+    "  Your browser does not support the video tag.\n",
+    "</video>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install opencv-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Video\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "\n",
+    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
+    "    cap = cv2.VideoCapture(video_path)\n",
+    "    if not cap.isOpened():\n",
+    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
+    "    \n",
+    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
+    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
+    "\n",
+    "    frames = []\n",
+    "    for idx in frame_indices:\n",
+    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
+    "        ret, frame = cap.read()\n",
+    "        if ret:\n",
+    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
+    "            if target_size:\n",
+    "                frames.append(resize_and_crop(frame, target_size))\n",
+    "            else:\n",
+    "                frames.append(frame)\n",
+    "    cap.release()\n",
+    "    return frames\n",
+    "\n",
+    "def resize_and_crop(image, target_size):\n",
+    "    width, height = image.size\n",
+    "    scale = target_size / min(width, height)\n",
+    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
+    "    left = (image.width - target_size) // 2\n",
+    "    top = (image.height - target_size) // 2\n",
+    "    return image.crop((left, top, left + target_size, top + target_size))\n",
+    "\n",
+    "# Video link\n",
+    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
+      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"Describe what the woman is doing.\"\n",
+    "\n",
+    "def generate_response(model, processor, frames, question):\n",
+    "\n",
+    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
+    "    messages = [\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
+    "        }\n",
+    "    ]\n",
+    "    inputs = processor(\n",
+    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
+    "        images=frames,\n",
+    "        return_tensors=\"pt\"\n",
+    "    ).to(model.device)\n",
+    "\n",
+    "    outputs = model.generate(\n",
+    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
+    "    )\n",
+    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
+    "\n",
+    "\n",
+    "# Extract frames from the video\n",
+    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
+    "\n",
+    "processor.image_processor.size = (384, 384)\n",
+    "processor.image_processor.do_resize = False\n",
+    "# Generate response\n",
+    "response = generate_response(model, processor, frames, question)\n",
+    "\n",
+    "# Display the result\n",
+    "# print(\"Question:\", question)\n",
+    "print(\"Response:\", response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 You're Done!\n",
+    "\n",
+    "This notebook demonstrated how to use a Visual-Language Model (VLM) such as formating prompts for multimodal tasks. By following the steps outlined here, you can experiment with VLMs and their applications.\n",
+    "\n",
+    "### Next Steps to Explore:\n",
+    "- Experiment with more use cases of VLMs.\n",
+    "- Collaborate with a colleague by reviewing their pull requests (PRs).\n",
+    "- Contribute to improving this course material by opening an issue or submitting a PR to introduce new use cases, examples, or concepts.\n",
+    "\n",
+    "Happy exploring! 🌟"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/vi/5_vision_language_models/vlm_finetuning.md b/vi/5_vision_language_models/vlm_finetuning.md
new file mode 100644
index 00000000..90ed8f76
--- /dev/null
+++ b/vi/5_vision_language_models/vlm_finetuning.md
@@ -0,0 +1,102 @@
+# VLM Fine-Tuning
+## Efficient Fine-Tuning
+
+### Quantization
+Quantization reduces the precision of model weights and activations, significantly lowering memory usage and speeding up computations. For example, switching from `float32` to `bfloat16` halves memory requirements per parameter while maintaining performance. For more aggressive compression, 8-bit and 4-bit quantization can be used, further reducing memory usage, though at the cost of some accuracy. These techniques can be applied to both the model and optimizer settings, enabling efficient training on hardware with limited resources.
+
+### PEFT & LoRA
+As introduced in Module 3, LoRA (Low-Rank Adaptation) focuses on learning compact rank-decomposition matrices while keeping the original model weights frozen. This drastically reduces the number of trainable parameters, cutting resource requirements significantly. LoRA, when integrated with PEFT, enables fine-tuning of large models by only adjusting a small, trainable subset of parameters. This approach is particularly effective for task-specific adaptations, reducing billions of trainable parameters to just millions while maintaining performance.
+
+### Batch Size Optimization
+To optimize the batch size for fine-tuning, start with a large value and reduce it if out-of-memory (OOM) errors occur. Compensate by increasing `gradient_accumulation_steps`, effectively maintaining the total batch size over multiple updates. Additionally, enable `gradient_checkpointing` to lower memory usage by recomputing intermediate states during the backward pass, trading computation time for reduced activation memory requirements. These strategies maximize hardware utilization and help overcome memory constraints.
+
+```python
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(
+    output_dir="./fine_tuned_model",  # Directory for model checkpoints
+    per_device_train_batch_size=4,   # Batch size per device (GPU/TPU)
+    num_train_epochs=3,              # Total training epochs
+    learning_rate=5e-5,              # Learning rate
+    save_steps=1000,                 # Save checkpoint every 1000 steps
+    bf16=True,                       # Use mixed precision for training
+    gradient_checkpointing=True,     # Enable to reduce activation memory usage
+    gradient_accumulation_steps=16,  # Accumulate gradients over 16 steps
+    logging_steps=50                 # Log metrics every 50 steps
+)
+```
+
+## **Supervised Fine-Tuning (SFT)**
+
+Supervised Fine-Tuning (SFT) adapts a pre-trained Vision Language Model (VLM) to specific tasks by leveraging labeled datasets containing paired inputs, such as images and corresponding text. This method enhances the model's ability to perform domain-specific or task-specific functions, such as visual question answering, image captioning, or chart interpretation.
+
+### **Overview**
+SFT is essential when you need a VLM to specialize in a particular domain or solve specific problems where the base model's general capabilities may fall short. For example, if the model struggles with unique visual features or domain-specific terminology, SFT allows it to focus on these areas by learning from labeled data.
+
+While SFT is highly effective, it has notable limitations:
+- **Data Dependency**: High-quality, labeled datasets tailored to the task are necessary.
+- **Computational Resources**: Fine-tuning large VLMs is resource-intensive.
+- **Risk of Overfitting**: Models can lose their generalization capabilities if fine-tuned too narrowly.
+
+Despite these challenges, SFT remains a robust technique for enhancing model performance in specific contexts.
+
+
+### **Usage**
+1. **Data Preparation**: Start with a labeled dataset that pairs images with text, such as questions and answers. For example, in tasks like chart analysis, the dataset `HuggingFaceM4/ChartQA` includes chart images, queries, and concise responses.
+
+2. **Model Setup**: Load a pre-trained VLM suitable for the task, such as `HuggingFaceTB/SmolVLM-Instruct`, and a processor for preparing text and image inputs. Configure the model for supervised learning and suitability for your hardware.
+
+3. **Fine-Tuning Process**:
+   - **Formatting Data**: Structure the dataset into a chatbot-like format, pairing system messages, user queries, and corresponding answers.
+   - **Training Configuration**: Use tools like Hugging Face's `TrainingArguments` or TRL's `SFTConfig` to set up training parameters. These include batch size, learning rate, and gradient accumulation steps to optimize resource usage.
+   - **Optimization Techniques**: Use **gradient checkpointing** to save memory during training. Use quantized model to reduce memory requirements and speed up computations.
+   - Employ `SFTTrainer` trainer from the TRL library, to streamline the training process.
+
+
+## Preference Optimization
+
+Preference Optimization, particularly Direct Preference Optimization (DPO), trains a Vision Language Model (VLM) to align with human preferences. Instead of strictly following predefined instructions, the model learns to prioritize outputs that humans subjectively prefer. This approach is particularly useful for tasks involving creative judgment, nuanced reasoning, or varying acceptable answers.
+
+
+### **Overview**
+Preference Optimization addresses scenarios where subjective human preferences are central to task success. By fine-tuning on datasets that encode human preferences, DPO enhances the model's ability to generate responses that are contextually and stylistically aligned with user expectations. This method is particularly effective for tasks like creative writing, customer interactions, or multi-choice scenarios.
+
+Despite its benefits, Preference Optimization has challenges:
+- **Data Quality**: High-quality, preference-annotated datasets are required, often making data collection a bottleneck.
+- **Complexity**: Training can involve sophisticated processes such as pairwise sampling of preferences and balancing computational resources.
+
+Preference datasets must capture clear preferences between candidate outputs. For example, a dataset may pair a question with two responses—one preferred and the other less acceptable. The model learns to predict the preferred response, even if it's not entirely correct, as long as it's better aligned with human judgment.
+
+
+### **Usage**
+1. **Dataset Preparation**  
+   A preference-labeled dataset is crucial for training. Each example typically consists of a prompt (e.g., an image and question) and two candidate responses: one chosen (preferred) and one rejected. For example:
+
+   - **Question**: How many families?  
+     - **Rejected**: The image does not provide any information about families.  
+     - **Chosen**: The image shows a Union Organization table setup with 18,000 families.  
+
+   The dataset teaches the model to prioritize better-aligned responses, even if they aren’t perfect. 
+
+2. **Model Setup**  
+   Load a pre-trained VLM and integrate it with Hugging Face's TRL library, which supports DPO, and a processor for preparing text and image inputs. Configure the model for supervised learning and suitability for your hardware.
+
+3. **Training Pipeline**  
+   Training involves configuring DPO-specific parameters. Here's a summary of the process:
+
+   - **Format Dataset**: Structure each sample with prompts, images, and candidate answers.
+   - **Loss Function**: Use a preference-based loss function to optimize the model for selecting the preferred output.
+   - **Efficient Training**: Combine techniques like quantization, gradient accumulation, and LoRA adapters to optimize memory and computation.
+
+
+
+## Resources
+
+- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) 
+- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
+- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
+- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
+
+## Next Steps
+
+⏩ Try the [vlm_finetune_sample.ipynb](./notebooks/vlm_finetune_sample.ipynb) to implement this unified approach to preference alignment.
\ No newline at end of file
diff --git a/vi/5_vision_language_models/vlm_usage.md b/vi/5_vision_language_models/vlm_usage.md
new file mode 100644
index 00000000..ef812927
--- /dev/null
+++ b/vi/5_vision_language_models/vlm_usage.md
@@ -0,0 +1,76 @@
+# Visual Language Models
+
+Visual Language Models (VLMs) bridge the gap between images and text, enabling advanced tasks like generating image captions, answering questions based on visuals, or understanding the relationship between textual and visual data. Their architecture is designed to process both modalities seamlessly.
+
+### Architecture
+
+VLMs combine image-processing components with text-generation models to achieve a unified understanding. The primary elements of their architecture are:
+
+![VLM Architecture](./images/VLM_Architecture.png)
+
+- **Image Encoder**: Transforms raw images into compact numerical representations. Pretrained encoders such as CLIP or vision transformers (ViT) are commonly used.
+- **Embedding Projector**: Maps image features into a space compatible with textual embeddings, often using dense layers or linear transformations.
+- **Text Decoder**: Acts as the language-generation component, translating fused multimodal information into coherent text. Examples include generative models like Llama or Vicuna.
+- **Multimodal Projector**: Provides an additional layer to blend image and text representations. It is critical for models like LLaVA to establish stronger connections between the two modalities.
+
+Most VLMs leverage pretrained image encoders and text decoders and align them through additional fine-tuning on paired image-text datasets. This approach makes training efficient while allowing the models to generalize effectively.
+
+### Usage
+
+![VLM Process](./images/VLM_Process.png)
+
+VLMs are applied to a range of multimodal tasks. Their adaptability allows them to perform in diverse domains with varying levels of fine-tuning:
+
+- **Image Captioning**: Generating descriptions for images.
+- **Visual Question Answering (VQA)**: Answering questions about the content of an image.
+- **Cross-Modal Retrieval**: Finding corresponding text for a given image or vice versa.
+- **Creative Applications**: Assisting in design, art generation, or creating engaging multimedia content.
+
+![VLM Usage](./images/VLM_Usage.png)
+
+Training and fine-tuning VLMs depend on high-quality datasets that pair images with text annotations. Tools like Hugging Face's `transformers` library provide convenient access to pretrained VLMs and streamlined workflows for custom fine-tuning.
+
+### Chat Format
+
+Many VLMs are structured to interact in a chatbot-like manner, enhancing usability. This format includes:
+
+- A **system message** that sets the role or context for the model, such as "You are an assistant analyzing visual data."
+- **User queries** that combine text inputs and associated images.
+- **Assistant responses** that provide text outputs derived from the multimodal analysis.
+
+This conversational structure is intuitive and aligns with user expectations, especially for interactive applications like customer service or educational tools.
+
+Here’s an example of how a formatted input might look:
+
+```json
+[
+    {
+        "role": "system",
+        "content": [{"type": "text", "text": "You are a Vision Language Model specialized in interpreting visual data from chart images..."}]
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "<image_data>"},
+            {"type": "text", "text": "What is the highest value in the bar chart?"}
+        ]
+    },
+    {
+        "role": "assistant",
+        "content": [{"type": "text", "text": "42"}]
+    }
+]
+```
+
+**Working with Multiple Images and Videos**
+
+VLMs can also process multiple images or even videos by adapting the input structure to accommodate sequential or parallel visual inputs. For videos, frames can be extracted and processed as individual images, while maintaining temporal order.
+
+## Resources
+
+- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
+- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm) 
+
+## Next Steps
+
+⏩ Try the [vlm_usage_sample.ipynb](./notebooks/vlm_usage_sample.ipynb) to try different usages of SMOLVLM.
\ No newline at end of file
diff --git a/vi/6_synthetic_datasets/README.md b/vi/6_synthetic_datasets/README.md
new file mode 100644
index 00000000..57e0d92a
--- /dev/null
+++ b/vi/6_synthetic_datasets/README.md
@@ -0,0 +1,39 @@
+# Synthetic Datasets
+
+Synthetic data is artificially generated data that mimics real-world usage. It allows overcoming data limitations by expanding or enhancing datasets. Even though synthetic data was already used for some use cases, large language models have made synthetic datasets more popular for pre- and post-training, and the evaluation of language models.
+
+We'll use [`distilabel`](https://distilabel.argilla.io/latest/), a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers. For a deeper dive into the package and best practices, check out the [documentation](https://distilabel.argilla.io/latest/).
+
+## Module Overview
+
+Synthetic data for language models can be categorized into three taxonomies: instructions, preferences and critiques. We will focus on the first two categories, which focus on the generation of datasets for instruction tuning and preference alignment. In both categories, we will cover aspects of the third category, which focuses on improving existing data with model critiques and rewrites.
+
+![Synthetic Data Taxonomies](./images/taxonomy-synthetic-data.png)
+
+## Contents
+
+### 1. [Instruction Datasets](./instruction_datasets.md)
+
+Learn how to generate instruction datasets for instruction tuning. We will explore creating instruction tuning datasets thorugh basic prompting and using prompts more refined techniques from papers. Instruction tuning datasets with seed data for in-context learning can be created through methods like SelfInstruct and Magpie. Additionally, we will explore instruction evolution through EvolInstruct. [Start learning](./instruction_datasets.md).
+
+### 2. [Preference Datasets](./preference_datasets.md)
+
+Learn how to generate preference datasets for preference alignment. We will build on top of the methods and techniques introduced in section 1, by generating additional responses. Next, we will learn how to improve such responses with the EvolQuality prompt. Finally, we will explore how to evaluate responses with the the UltraFeedback prompt which will produce a score and critique, allowing us to create preference pairs. [Start learning](./preference_datasets.md).
+
+### Exercise Notebooks
+
+| Title | Description | Exercise | Link | Colab |
+|-------|-------------|----------|------|-------|
+| Instruction Dataset | Generate a dataset for instruction tuning | 🐢 Generate an instruction tuning dataset <br> 🐕 Generate a dataset for instruction tuning with seed data <br> 🦁 Generate a dataset for instruction tuning with seed data and with instruction evolution | [Link](./notebooks/instruction_sft_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
+| Preference Dataset | Generate a dataset for preference alignment | 🐢 Generate a preference alignment dataset <br> 🐕 Generate a preference alignment dataset with response evolution <br> 🦁 Generate a preference alignment dataset with response evolution and critiques  | [Link](./notebooks/preference_alignment_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
+
+## Resources
+
+- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Synthetic Data Generator is UI app](https://huggingface.co/blog/synthetic-data-generator)
+- [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
+- [Self-instruct](https://arxiv.org/abs/2212.10560)
+- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
+- [Magpie](https://arxiv.org/abs/2406.08464)
+- [UltraFeedback](https://arxiv.org/abs/2310.01377)
+- [Deita](https://arxiv.org/abs/2312.15685)
diff --git a/vi/6_synthetic_datasets/images/pipeline.png b/vi/6_synthetic_datasets/images/pipeline.png
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diff --git a/vi/6_synthetic_datasets/instruction_datasets.md b/vi/6_synthetic_datasets/instruction_datasets.md
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+# Generating Instruction Datasets
+
+Within [the chapter on instruction tuning](../1_instruction_tuning/README.md), we learned about fine-tuning models with Supervised Fine-tuning. In this section, we will explore how to generate instruction datasets for SFT. We will explore creating instruction tuning datasets through basic prompting and using more refined techniques from papers. Instruction tuning datasets with seed data for in-context learning can be created through methods like SelfInstruct and Magpie. Additionally, we will explore instruction evolution through EvolInstruct. Lastly, we will explore how to generate a dataset for instruction tuning using a distilabel pipeline.
+
+## From Prompt to Data
+
+Synthetic data sounds fancy, but it can be simplified as creating data through effective prompting to extract knowledge from a model. In turn, you can think of this as a way to generate data for a specific task. The challenge is prompting effectively while ensuring the data is diverse and representative. Fortunately, many papers have explored this problem, and we will explore some of the useful ones during this course. First things first, we will explore how to generate synthetic data through manual prompting.
+
+### Basic Prompting
+
+Let's start with a basic example and load the [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) model using the `transformers` integration of the `distilabel` library. We will use the `TextGeneration` class to generate a synthetic `prompt` and use that to generate a `completion`.
+
+Next, we will load the model using the `distilabel` library.
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import TextGeneration
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+gen = TextGeneration(llm=llm)
+gen.load()
+```
+
+!!! note
+    Distilabel loads the `llm` into memory, so, when working in a notebook, we need to `gen.unload()` after we are done with it to avoid memory issues.
+
+We will now use the `llm` to generate a `prompt` for instruction tuning.
+
+```python
+next(gen.process([{"instruction": "Generate a questions about the Hugging Face Smol-Course on small AI models."}]))
+# What is the purpose of Smol-Course?
+```
+
+Lastly, we can use that same `prompt` as input to generate a `completion`.
+
+```python
+next(gen.process([{"instruction": "What is the purpose of Smol-Course?"}]))
+# The Smol-Course is a platform designed to learning computer science concepts.
+```
+
+Cool! We can generated a synthetic `prompt` and a corresponding `completion`. Re-using this simple approach at scale will allow us to generate a lot more data however, the quality of the data is not that great and does not take into account the nuances of our course or domain. Additionally, re-running the current code shows us the data is not that diverse. Luckily, there are ways to solve this problem.
+
+### SelfInstruct
+
+SelfInstruct is a prompt that generates new instructions based on a seed dataset. This seed data can be a single instruction or a piece of context. The process begins with a pool of initial seed data. The language model is then prompted to generate new instructions based on this seed data using in-context learning. The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) and a simplified version is shown below:
+
+```
+# Task Description
+Develop {{ num_instructions }} user queries that can be received by the given AI application and applicable to the provided context. Emphasize diversity in verbs and linguistic structures within the model's textual capabilities.
+
+# Context
+{{ input }}
+
+# Output
+```
+
+To use it, we need to pass the `llm` to the [SelfInstruct class](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/). Let's use the text from the [Prompt to Data section](#prompt-to-data) as context and generate a new instruction.
+
+```python
+from distilabel.steps.tasks import SelfInstruct
+
+self_instruct = SelfInstruct(llm=llm)
+self_instruct.load()
+
+context = "<prompt_to_data_section>"
+
+next(self_instruct.process([{"input": text}]))["instructions"][0]
+# What is the process of generating synthetic data through manual prompting?
+```
+
+The generated instruction is a lot better already and it fits our actual content and domain. However, we can do even better by improving the prompt through evolution.
+
+### EvolInstruct
+
+EvolInstruct is a prompting technique that takes an input instruction and evolves it into a better version of the same instruction. This better version is defined according to a set of criteria and adds constraints, deepening, concretizing, reasoning or complications to the original instruction. The process can be repeated multiple times to create various evolutions of the same instruction, ideally leading to a better version of the original instruction. The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct) and a simplified version is shown below:
+
+```
+I want you act as a Prompt Rewriter.
+Given prompt a prompt, rewrite it into a more complex version.
+Complicate the prompt based on the following criteria:
+{{ criteria }}
+
+# Prompt
+{{ input }}
+
+# Output
+```
+
+To use it, we need to pass the `llm` to the [EvolInstruct class](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/). Let's use the synthetic prompt from [the SelfInstruct section](#selfinstruct) as input and evolve it into a better version. For this example, we will only evolve for one generation.
+
+```python
+from distilabel.steps.tasks import EvolInstruct
+
+evol_instruct = EvolInstruct(llm=llm, num_evolutions=1)
+evol_instruct.load()
+
+text = "What is the process of generating synthetic data through manual prompting"
+
+next(evol_instruct.process([{"instruction": text}]))
+# What is the process of generating synthetic data through manual prompting?
+# And, how does the artificial intelligence system, GPT4, use machine learning algorithms to manipulate the input data into synthetic data?
+```
+
+The instruction is now more complex but has lost some of the original meaning. So, take into account that evolving can be a double-edged sword and we need to be careful with the quality of the data we generate.
+
+### Magpie
+
+Magpie is a technique that relies on the auto-regressive factors of language model and the [chat-template](../1_instruction_tuning/chat_templates.md) that has been using during the instruction tuning process. As you might remember, the chat-template is a format that structures conversations with clear role indicators (system, user, assistant). During the instruction tuning phase, the language model has been optimized to reproduce this format and that is exactly what magpie takes advantage of. It starts with a pre-query-prompt based on the chat-template but it stops before the user message indicator, e.g. `<|im_start|>user\n`, and then it uses the language model to generate the user prompt until the end of the assistant indicator, e.g. `<|im_end|>`. This approach allows us to generate a lot of data in a very efficient way and it can even be scaled up to multi-turn conversations. It is hypothesized this generated data reproduces training data from the instruction tuning phase of the model used.
+
+In this scenario, prompt templates differ per model because they are based on the chat-template format. But we can walk through a simplified version of the process step-by-step.
+
+```bash
+# Step 1: provide the pre-query-prompt
+<|im_start|>user\n
+
+# Step 2: the language model generates the user prompt
+<|im_start|>user\n
+What is the purpose of Smol-Course?
+
+# Step 3: stop the generation
+<|im_end|>
+```
+
+To use it in distilabel, we need to pass the `llm` to the [Magpie class](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/).
+
+```python
+from distilabel.steps.tasks import Magpie
+
+magpie = Magpie(llm=llm)
+magpie.load()
+
+next(magpie.process([{"system_prompt": "You are a helpful assistant."}]))
+# [{
+#   "role": "user",
+#   "content": "Can you provide me with a list of the top 3 universities?"
+# },
+# {
+#   "role": "assistant",
+#   "content": "The top 3 universities are: MIT, Yale, Stanford."
+# }]
+```
+
+
+We immediately get a dataset with a `prompt` and `completion` . To improve the performance on our own domain, we can inject additional context into the `system_prompt`. For the LLM to generate specific domain data in combination with Magpie, it helps describing in the system prompt what the users queries will be. This is then used in the  pre-query-prompt before we start generating the user prompt and bias the LLM to generate user queries of that domain.
+
+```
+You're an AI assistant that will help users solving math problems.
+```
+
+It's important to write the system prompt as shown above instead of something like:
+
+```
+You're an AI assistant that generates math problems
+```
+
+Generally, language models are less optimized for passing additional context to the `system_prompt` so this does not always work as well for customisation as other techniques.
+
+### From Prompts to Pipelines
+
+The classes we've seen so far are all standalone classes that can be used in a pipeline. This is a good start, but we can do even better by using the `Pipeline` class to generate a dataset. We will use the `TextGeneration` step to generate a synthetic dataset for instruction tuning. The pipeline will consist of a `LoadDataFromDicts` step to load the data, a `TextGeneration` step to generate the `prompt` and a `completion` for that prompt. We will connect the steps and flow the data through the pipeline using the `>>` operator. Within the [documentation of distilabel](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/#input-output-columns) we can see input and output columns of the step. We to ensure that the data flow correctly through the pipeline, we will use the `output_mappings` parameter to map the output columns to the input columns of the next step.
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps import LoadDataFromDicts
+from distilabel.steps.tasks import TextGeneration
+
+with Pipeline() as pipeline:
+    data = LoadDataFromDicts(data=[{"instruction": "Generate a short question about the Hugging Face Smol-Course."}])
+    llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+    gen_a = TextGeneration(llm=llm, output_mappings={"generation": "instruction"})
+    gen_b = TextGeneration(llm=llm, output_mappings={"generation": "response"})
+    data >> gen_a >> gen_b
+
+if __name__ == "__main__":
+    distiset = pipeline.run(use_cache=False)
+    print(distiset["default"]["train"][0])
+# [{
+#   "instruction": "What is the purpose of Smol-Course?",
+#   "response": "The Smol-Course is a platform designed to learning computer science concepts."
+# }]
+```
+
+Under the hood, this pipeline has a lot of cool features. It automatically caches generation results, so we can don't have to re-run the generation steps. There is included fault-tolerance, so if the generation steps fail, the pipeline will continue to run. And the pipeline exexutes all generation steps in parallel, so the generation is faster. We can even visualise the pipeline using the `draw` method. Here you can see how the data flows through the pipeline and how the `output_mappings` are used to map the output columns to the input columns of the next step.
+
+![Pipeline](./images/pipeline.png)
+
+## Best Practices
+
+- Ensure you have a diverse seed data to cover a wide range of scenarios
+- Regularly evaluate the dataset to ensure generated data is diverse and of high quality
+- Iterate on the (system)prompt to improve the quality of the data
+
+## Next Steps
+
+👨🏽‍💻 Code -[Exercise Notebook](./notebooks/instruction_sft_dataset.ipynb) to generate a dataset for instruction tuning
+🧑‍🏫 Learn - About [generating preference datasets](./preference_datasets.md)
+
+## References
+
+- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Self-instruct](https://arxiv.org/abs/2212.10560)
+- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
+- [Magpie](https://arxiv.org/abs/2406.08464)
\ No newline at end of file
diff --git a/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
new file mode 100644
index 00000000..54584722
--- /dev/null
+++ b/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate a dataset for instruction tuning\n",
+    "\n",
+    "This notebook will guide you through the process of generating a dataset for instruction tuning. We'll use the `distilabel` package to generate a dataset for instruction tuning.\n",
+    "\n",
+    "So let's dig in to some instruction tuning datasets.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercise: Generate a dataset for instruction tuning</h2>\n",
+    "    <p>Now that you've seen how to generate a dataset for instruction tuning, try generating a dataset for instruction tuning.</p>\n",
+    "    <p><b>Difficulty Levels</b></p>\n",
+    "    <p>🐢 Generate an instruction tuning dataset</p>\n",
+    "    <p>🐕 Generate a dataset for instruction tuning with seed data</p>\n",
+    "    <p>🦁 Generate a dataset for instruction tuning with seed data and with instruction evolution</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## Install dependencies\n",
+    "\n",
+    "Instead of transformers, you can also install `vllm` or `hf-inference-endpoints`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start synthesizing\n",
+    "\n",
+    "As we've seen in the previous course content, we can create a distilabel pipelines for instruction dataset generation. The bare minimum pipline is already provided. Make sure to scale up this pipeline to generate a large dataset for instruction tuning. Swap out models, model providers and generation arguments to see how they affect the quality of the dataset. Experiment small, scale up later.\n",
+    "\n",
+    "Check out the [distilabel components gallery](https://distilabel.argilla.io/latest/components-gallery/) for information about the processing classes and how to use them. \n",
+    "\n",
+    "An example of loading data from the Hub instead of dictionaries is provided below.\n",
+    "\n",
+    "```python\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "with Pipeline(...) as pipeline:\n",
+    "    ...\n",
+    "\n",
+    "if __name__ == \"__main__:\n",
+    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
+    "    distiset = pipeline.run(dataset=dataset)\n",
+    "```\n",
+    "\n",
+    "Don't forget to push your dataset to the Hub after running the pipeline!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distilabel.llms import TransformersLLM\n",
+    "from distilabel.pipeline import Pipeline\n",
+    "from distilabel.steps import LoadDataFromDicts\n",
+    "from distilabel.steps.tasks import TextGeneration\n",
+    "\n",
+    "with Pipeline() as pipeline:\n",
+    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Generate a short question about the Hugging Face Smol-Course.\"}])\n",
+    "    llm = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
+    "    gen_a = TextGeneration(llm=llm, output_mappings={\"generation\": \"instruction\"})\n",
+    "    gen_b = TextGeneration(llm=llm, output_mappings={\"generation\": \"response\"})\n",
+    "    data >> gen_a >> gen_b\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    distiset = pipeline.run(use_cache=False)\n",
+    "    distiset.push_to_hub(\"huggingface-smol-course-instruction-tuning-dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌯 That's a wrap\n",
+    "\n",
+    "You've now seen how to generate a dataset for instruction tuning. You could use this to:\n",
+    "\n",
+    "- Generate a dataset for instruction tuning.\n",
+    "- Create evaluation datasets for instruction tuning.\n",
+    "\n",
+    "Next\n",
+    "\n",
+    "🧑‍🏫 Learn - About [generating preference datasets](./preference_datasets.md)\n",
+    "🏋️‍♂️ Fine-tune a model for instruction tuning with a synthetic dataset based on the [instruction tuning chapter](../../1_instruction_tuning/README.md)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
new file mode 100644
index 00000000..cd9747df
--- /dev/null
+++ b/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate a dataset for preference alignment\n",
+    "\n",
+    "This notebook will guide you through the process of generating a dataset for preference alignment. We'll use the `distilabel` package to generate a dataset for preference alignment.\n",
+    "\n",
+    "So let's dig in to some preference alignment datasets.\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>Exercise: Generate a dataset for preference alignment</h2>\n",
+    "    <p>Now that you've seen how to generate a dataset for preference alignment, try generating a dataset for preference alignment.</p>\n",
+    "    <p><b>Difficulty Levels</b></p>\n",
+    "    <p>🐢 Generate a dataset for preference alignment</p>\n",
+    "    <p>🐕 Generate a dataset for preference alignment with response evolution</p>\n",
+    "    <p>🦁 Generate a dataset for preference alignment with response evolution and model pooling</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## Install dependencies\n",
+    "\n",
+    "Instead of transformers, you can also install `vllm` or `hf-inference-endpoints`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start synthesizing\n",
+    "\n",
+    "As we've seen in the previous notebook, we can create a distilabel pipeline for preference dataset generation. The bare minimum pipline is already provided. You can continue work on this pipeline to generate a large dataset for preference alignment. Swap out models, model providers and generation arguments to see how they affect the quality of the dataset. Experiment small, scale up later.\n",
+    "\n",
+    "Check out the [distilabel components gallery](https://distilabel.argilla.io/latest/components-gallery/) for information about the processing classes and how to use them. \n",
+    "\n",
+    "An example of loading data from the Hub instead of dictionaries is provided below.\n",
+    "\n",
+    "```python\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "with Pipeline(...) as pipeline:\n",
+    "    ...\n",
+    "\n",
+    "if __name__ == \"__main__:\n",
+    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
+    "    distiset = pipeline.run(dataset=dataset)\n",
+    "```\n",
+    "\n",
+    "Don't forget to push your dataset to the Hub after running the pipeline!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distilabel.llms import TransformersLLM\n",
+    "from distilabel.pipeline import Pipeline\n",
+    "from distilabel.steps import GroupColumns, LoadDataFromDicts\n",
+    "from distilabel.steps.tasks import TextGeneration\n",
+    "\n",
+    "with Pipeline() as pipeline:\n",
+    "    data = LoadDataFromDicts(data=[{\"instruction\": \"What is synthetic data?\"}])\n",
+    "    llm_a = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
+    "    gen_a = TextGeneration(llm=llm_a)\n",
+    "    llm_b = TransformersLLM(model=\"Qwen/Qwen2.5-1.5B-Instruct\")\n",
+    "    gen_b = TextGeneration(llm=llm_b)\n",
+    "    group = GroupColumns(columns=[\"generation\"])\n",
+    "    data >> [gen_a, gen_b] >> group\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    distiset = pipeline.run()\n",
+    "    distiset.push_to_hub(\"huggingface-smol-course-preference-tuning-dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌯 That's a wrap\n",
+    "\n",
+    "You've now seen how to generate a dataset for preference alignment. You could use this to:\n",
+    "\n",
+    "- Generate a dataset for preference alignment.\n",
+    "- Create evaluation datasets for preference alignment.\n",
+    "\n",
+    "Next\n",
+    "\n",
+    "🏋️‍♂️ Fine-tune a model with preference alignment with a synthetic dataset based on the [preference tuning chapter](../../2_preference_alignment/README.md) \n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/vi/6_synthetic_datasets/preference_datasets.md b/vi/6_synthetic_datasets/preference_datasets.md
new file mode 100644
index 00000000..38bb0e02
--- /dev/null
+++ b/vi/6_synthetic_datasets/preference_datasets.md
@@ -0,0 +1,155 @@
+# Generating Preference Datasets
+
+Within [the chapter on preference alignment](../2_preference_alignment/README.md), we learned about Direct Preference Optimization. In this section, we will explore how to generate preference datasets for methods like DPO. We will build on top of the methods that were introduced in [generating instruction datasets](./instruction_datasets.md). Additionally, we will show how to add extra completions to the dataset using basic prompting or by using EvolQuality to improve the quality of responses. Lastly, we will show how UltraFeedback can be used to generate scores and critiques.
+
+## Creating multiple completions
+
+Preference data is a dataset with multiple `completions` for the same `instruction`. We can add more `completions` to a dataset by prompting a model to generate them. When doing this, we need to ensure that the second completion is not too similar to the first completion in terms of overall quality and phrasing. This is important because the model needs to be optimized for a clear preference. We want to know which completion is preferred over the other, normally referred to as `chosen` and `rejected`. We will go into more detail about determining chosen and rejected completions in the [section on creating scores](#creating-scores).
+
+### Model pooling
+
+You can use models from different model families to generate a second completion, which is called model pooling. To further improve the quality of the second completion, you can use different generation arguments, like tweaking the `temperature`. Lastly, you can use different prompt templates or system prompts to generate a second completion to ensure diversity based on specific characteristics defined in the template. In theory, we could take two models of varying quality and use the better one as the `chosen` completion.
+
+Let's start with model pooling by loading the [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) and [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) models using the `transformers` integration of the `distilabel` library. Using these models, we will create two synthetic `responses` for a given `prompt`. We will create another pipeline with `LoadDataFromDicts`, `TextGeneration`, and `GroupColumns`. We will first load data, then use two generation steps, and then group the results. We connect the steps and flow the data through the pipeline using the `>>` operator and `[]`, which means that we want to use the output of the previous step as the input for both steps within the list.
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps import GroupColumns, LoadDataFromDicts
+from distilabel.steps.tasks import TextGeneration
+
+with Pipeline() as pipeline:
+    data = LoadDataFromDicts(data=[{"instruction": "What is synthetic data?"}])
+    llm_a = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+    gen_a = TextGeneration(llm=llm_a)
+    llm_b = TransformersLLM(model="Qwen/Qwen2.5-1.5B-Instruct")
+    gen_b = TextGeneration(llm=llm_b)
+    group = GroupColumns(columns=["generation"])
+    data >> [gen_a, gen_b] >> group
+
+if __name__ == "__main__":
+    distiset = pipeline.run()
+    print(distiset["default"]["train"]["grouped_generation"][0])
+# {[
+#   'Synthetic data is artificially generated data that mimics real-world usage.',
+#   'Synthetic data refers to data that has been generated artificially.'
+# ]}
+```
+
+As you can see, we have two synthetic `completions` for the given `prompt`. We could have boosted diversity by initializing the `TextGeneration` steps with a specific `system_prompt` or by passing generation arguments to the `TransformersLLM`. Let's now see how we can improve the quality of the `completions` using EvolQuality.
+
+### EvolQuality
+
+EvolQuality is similar to [EvolInstruct](./instruction_datasets.md#evolinstruct) - it is a prompting technique but it evolves `completions` instead of the input `prompt`. The task takes both a `prompt` and `completion` and evolves the `completion` into a version that better responds to the `prompt` based on a set of criteria. This better version is defined according to criteria for improving helpfulness, relevance, deepening, creativity, or details. Because this automatically generates a second completion, we can use it to add more `completions` to a dataset. In theory, we could even assume the evolution is better than the original completion and use it as the `chosen` completion out of the box.
+
+The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality) and a simplified version is shown below:
+
+```bash
+I want you act as a Response Rewriter.
+Given prompt a and a response, rewrite the response into a better version.
+Complicate the prompt based on the following criteria:
+{{ criteria }}
+
+# Prompt
+{{ input }}
+
+# Response
+{{ output }}
+
+# Improved Response
+```
+
+Let's use the [EvolQuality class](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) to evolve the synthetic `prompt` and `completion` from [the Model Pooling section](#model-pooling) into a better version. For this example, we will only evolve for one generation.
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import EvolQuality
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+evol_quality = EvolQuality(llm=llm, num_evolutions=1)
+evol_quality.load()
+
+instruction = "What is synthetic data?"
+completion = "Synthetic data is artificially generated data that mimics real-world usage."
+
+next(evol_quality.process([{
+    "instruction": instruction,
+    "response": completion
+}]))
+# The process of generating synthetic data through manual prompting involves creating artificial data sets that mimic real-world usage patterns.
+```
+
+The `response` is now more complex and specific to the `instruction`. This is a good start, but as we have seen with EvolInstruct, evolved generations are not always better. Hence, it is important to use additional evaluation techniques to ensure the quality of the dataset. We will explore this in the next section.
+
+## Creating Scores
+
+Scores are a measure of how much one response is preferred over another. In general, these scores can be absolute, subjective, or relative. For this course, we will focus on the first two because they are most valuable for creating preference datasets. This scoring is a way of judging and evaluating using language models and therefore has some overlap with the evaluation techniques we have seen in [the chapter on evaluation](../3_evaluation/README.md). As with the other evaluation techniques, scores and evaluations normally require larger models to better align with human preferences.
+
+### UltraFeedback
+
+UltraFeedback is a technique that generates scores and critiques for a given `prompt` and its `completion`.
+
+The scores are based on the quality of the `completion` according to a set of criteria. There are four fine-grained criteria: `helpfulness`, `relevance`, `deepening`, and `creativity`. These are useful but generally speaking, using the overall criteria is a good start, which allows us to simplify the process of generating scores. The scores can be used to determine which `completion` is the `chosen` and which is the `rejected` one. Because they are absolute, they can also be used as interesting filters for outliers in the dataset, either finding the worst completions or the pairs with more or less difference.
+
+The critiques are added to provide reasoning for the score. They can be used as extra context to help us understand the differences between the scores. The language model generates extensive critiques which is very useful, but this also introduces extra cost and complexity to the process because generating critiques is more expensive than generating a single token to represent a score.
+
+The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback) and a simplified version is shown below:
+
+```bash
+Evaluate the model's outputs based on various criteria: Helpfulness, Relevance, Deepening, Creativity
+Your role is to provide a holistic assessment based on the above factors.
+Score the output from 1 to 5 on overall quality.
+
+Answer with the following format: score - rationale
+
+# Input
+{{ input }}
+
+# Response
+{{ output }}
+
+# Score - Rationale
+```
+
+Let's use the [UltraFeedback class](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) to evaluate the synthetic `prompt` and `completion` from [the Model Pooling section](#model-pooling).
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import UltraFeedback
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+ultrafeedback = UltraFeedback(llm=llm)
+ultrafeedback.load()
+
+instruction = "What is synthetic data?"
+completion_a = "Synthetic data is artificially generated data that mimics real-world usage."
+completion_b = "Synthetic data refers to data that has been generated artificially."
+
+next(ultrafeedback.process([{
+    "instruction": instruction,
+    "generations": [completion_a, completion_b]
+}]))
+# [
+#     {
+#         'ratings': [4, 5],
+#         'rationales': ['could have been more specific', 'good definition'],
+#     }
+# ]
+```
+
+## Best Practices
+
+- Overall scores are cheaper and easier to generate than critiques and specific scores
+- Use bigger models to generate scores and critiques
+- Use a diverse set of models to generate scores and critiques
+- Iterate on configuration of the `system_prompt` and models
+
+## Next Steps
+
+👨🏽‍💻 Code -[Exercise Notebook](./notebooks/preference_dpo_dataset.ipynb) to generate a dataset for instruction tuning
+
+## References
+
+- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Deita](https://arxiv.org/abs/2312.15685)
+- [UltraFeedback](https://arxiv.org/abs/2310.01377)

From 7d09c5de9ab3f393bf1126a11a96c053b7f2f280 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Fri, 17 Jan 2025 22:20:45 +0100
Subject: [PATCH 48/63] init

---
 vi/5_vision_language_models/vlm_finetuning.md | 128 +++++++++---------
 1 file changed, 66 insertions(+), 62 deletions(-)

diff --git a/vi/5_vision_language_models/vlm_finetuning.md b/vi/5_vision_language_models/vlm_finetuning.md
index 90ed8f76..28f57b5c 100644
--- a/vi/5_vision_language_models/vlm_finetuning.md
+++ b/vi/5_vision_language_models/vlm_finetuning.md
@@ -1,102 +1,106 @@
-# VLM Fine-Tuning
-## Efficient Fine-Tuning
+# Tinh chỉnh Mô hình Ngôn ngữ Thị giác (VLM)
 
-### Quantization
-Quantization reduces the precision of model weights and activations, significantly lowering memory usage and speeding up computations. For example, switching from `float32` to `bfloat16` halves memory requirements per parameter while maintaining performance. For more aggressive compression, 8-bit and 4-bit quantization can be used, further reducing memory usage, though at the cost of some accuracy. These techniques can be applied to both the model and optimizer settings, enabling efficient training on hardware with limited resources.
+## Tinh chỉnh Hiệu quả
+
+### Lượng tử hóa (Quantization)
+
+Lượng tử hóa làm giảm độ chính xác của trọng số mô hình (model weights) và hàm kích hoạt (activations), giúp giảm đáng kể mức sử dụng bộ nhớ và tăng tốc độ tính toán. Ví dụ: chuyển từ `float32` sang `bfloat16` giúp giảm một nửa yêu cầu bộ nhớ cho mỗi tham số trong khi vẫn duy trì hiệu suất. Để nén mạnh hơn, có thể sử dụng lượng tử hóa `8 bit` và `4 bit`, giúp giảm mức sử dụng bộ nhớ hơn nữa, mặc dù phải đánh đổi bằng việc giảm độ chính xác. Những kỹ thuật này có thể được áp dụng cho cả trọng số mô hình và trình tối ưu hóa (optimizer), cho phép huấn luyện hiệu quả trên phần cứng có tài nguyên hạn chế.
 
 ### PEFT & LoRA
-As introduced in Module 3, LoRA (Low-Rank Adaptation) focuses on learning compact rank-decomposition matrices while keeping the original model weights frozen. This drastically reduces the number of trainable parameters, cutting resource requirements significantly. LoRA, when integrated with PEFT, enables fine-tuning of large models by only adjusting a small, trainable subset of parameters. This approach is particularly effective for task-specific adaptations, reducing billions of trainable parameters to just millions while maintaining performance.
 
-### Batch Size Optimization
-To optimize the batch size for fine-tuning, start with a large value and reduce it if out-of-memory (OOM) errors occur. Compensate by increasing `gradient_accumulation_steps`, effectively maintaining the total batch size over multiple updates. Additionally, enable `gradient_checkpointing` to lower memory usage by recomputing intermediate states during the backward pass, trading computation time for reduced activation memory requirements. These strategies maximize hardware utilization and help overcome memory constraints.
+Như đã giới thiệu trong Bài 3, LoRA (Low-Rank Adaptation) tập trung vào việc học các ma trận phân rã hạng thấp (rank-decomposition matrices) nhỏ gọn trong khi vẫn giữ nguyên trọng số của mô hình gốc. Điều này làm giảm đáng kể số lượng tham số có thể huấn luyện, giảm đáng kể yêu cầu tài nguyên. LoRA, khi được tích hợp với PEFT (Parameter-Efficient Fine-Tuning), cho phép tinh chỉnh các mô hình lớn bằng cách chỉ điều chỉnh một tập hợp con nhỏ các tham số có thể huấn luyện. Phương pháp này đặc biệt hiệu quả cho các thích ứng theo nhiệm vụ cụ thể, giảm hàng tỷ tham số có thể huấn luyện xuống chỉ còn hàng triệu trong khi vẫn duy trì hiệu suất.
+
+### Tối ưu hóa Kích thước Batch (Batch Size)
+
+Để tối ưu hóa kích thước batch cho quá trình tinh chỉnh, hãy bắt đầu với một giá trị lớn và giảm nếu xảy ra lỗi out-of-memory (OOM). Bù lại bằng cách tăng `gradient_accumulation_steps`, duy trì hiệu quả tổng kích thước batch trên nhiều lần cập nhật. Ngoài ra, hãy bật `gradient_checkpointing` để giảm mức sử dụng bộ nhớ bằng cách tính toán lại các trạng thái trung gian trong quá trình lan truyền ngược (backward pass), đánh đổi thời gian tính toán để giảm yêu cầu bộ nhớ kích hoạt. Những chiến lược này tối đa hóa việc sử dụng phần cứng và giúp khắc phục các hạn chế về bộ nhớ.
 
 ```python
 from transformers import TrainingArguments
 
 training_args = TrainingArguments(
-    output_dir="./fine_tuned_model",  # Directory for model checkpoints
-    per_device_train_batch_size=4,   # Batch size per device (GPU/TPU)
-    num_train_epochs=3,              # Total training epochs
-    learning_rate=5e-5,              # Learning rate
-    save_steps=1000,                 # Save checkpoint every 1000 steps
-    bf16=True,                       # Use mixed precision for training
-    gradient_checkpointing=True,     # Enable to reduce activation memory usage
-    gradient_accumulation_steps=16,  # Accumulate gradients over 16 steps
-    logging_steps=50                 # Log metrics every 50 steps
+    output_dir="./fine_tuned_model",  # Thư mục cho các checkpoint của mô hình
+    per_device_train_batch_size=4,   # Kích thước batch trên mỗi thiết bị (GPU/TPU)
+    num_train_epochs=3,              # Tổng số epoch huấn luyện
+    learning_rate=5e-5,              # Tốc độ học
+    save_steps=1000,                 # Lưu checkpoint sau mỗi 1000 bước
+    bf16=True,                       # Sử dụng mixed precision để huấn luyện
+    gradient_checkpointing=True,     # Bật để giảm mức sử dụng bộ nhớ kích hoạt
+    gradient_accumulation_steps=16,  # Tích lũy gradient qua 16 bước
+    logging_steps=50                 # Ghi nhật ký các số liệu sau mỗi 50 bước
 )
 ```
 
-## **Supervised Fine-Tuning (SFT)**
+## **Tinh chỉnh có Giám sát (Supervised Fine-Tuning - SFT)**
 
-Supervised Fine-Tuning (SFT) adapts a pre-trained Vision Language Model (VLM) to specific tasks by leveraging labeled datasets containing paired inputs, such as images and corresponding text. This method enhances the model's ability to perform domain-specific or task-specific functions, such as visual question answering, image captioning, or chart interpretation.
+Tinh chỉnh có Giám sát (SFT) điều chỉnh Mô hình Ngôn ngữ Thị giác (VLM) đã được huấn luyện trước cho các nhiệm vụ cụ thể bằng cách tận dụng các tập dữ liệu được gán nhãn có chứa các đầu vào được ghép nối, chẳng hạn như hình ảnh và văn bản tương ứng. Phương pháp này nâng cao khả năng của mô hình để thực hiện các chức năng cụ thể theo miền (domain-specific) hoặc theo nhiệm vụ (task-specific), chẳng hạn như trả lời câu hỏi bằng hình ảnh, chú thích hình ảnh hoặc diễn giải biểu đồ.
 
-### **Overview**
-SFT is essential when you need a VLM to specialize in a particular domain or solve specific problems where the base model's general capabilities may fall short. For example, if the model struggles with unique visual features or domain-specific terminology, SFT allows it to focus on these areas by learning from labeled data.
+### **Tổng quan**
 
-While SFT is highly effective, it has notable limitations:
-- **Data Dependency**: High-quality, labeled datasets tailored to the task are necessary.
-- **Computational Resources**: Fine-tuning large VLMs is resource-intensive.
-- **Risk of Overfitting**: Models can lose their generalization capabilities if fine-tuned too narrowly.
+SFT là cần thiết khi bạn cần một VLM chuyên về một lĩnh vực cụ thể hoặc giải quyết các vấn đề cụ thể mà khả năng chung của mô hình cơ sở có thể không đáp ứng được. Ví dụ: nếu mô hình gặp khó khăn với các đặc điểm hình ảnh độc đáo hoặc thuật ngữ chuyên ngành, SFT cho phép mô hình tập trung vào các lĩnh vực này bằng cách học từ dữ liệu được gán nhãn.
 
-Despite these challenges, SFT remains a robust technique for enhancing model performance in specific contexts.
+Mặc dù SFT rất hiệu quả, nó có những hạn chế đáng chú ý:
 
+- **Phụ thuộc vào dữ liệu**: Cần có các tập dữ liệu được gán nhãn chất lượng cao phù hợp với nhiệm vụ.
+- **Tài nguyên tính toán**: Tinh chỉnh các VLM lớn đòi hỏi nhiều tài nguyên.
+- **Nguy cơ quá khớp (Overfitting)**: Các mô hình có thể mất khả năng khái quát hóa nếu được tinh chỉnh quá hẹp.
 
-### **Usage**
-1. **Data Preparation**: Start with a labeled dataset that pairs images with text, such as questions and answers. For example, in tasks like chart analysis, the dataset `HuggingFaceM4/ChartQA` includes chart images, queries, and concise responses.
+Tuy vậy, SFT vẫn là một kỹ thuật mạnh mẽ để nâng cao hiệu suất của mô hình trong các bối cảnh cụ thể.
 
-2. **Model Setup**: Load a pre-trained VLM suitable for the task, such as `HuggingFaceTB/SmolVLM-Instruct`, and a processor for preparing text and image inputs. Configure the model for supervised learning and suitability for your hardware.
+### **Cách sử dụng**
 
-3. **Fine-Tuning Process**:
-   - **Formatting Data**: Structure the dataset into a chatbot-like format, pairing system messages, user queries, and corresponding answers.
-   - **Training Configuration**: Use tools like Hugging Face's `TrainingArguments` or TRL's `SFTConfig` to set up training parameters. These include batch size, learning rate, and gradient accumulation steps to optimize resource usage.
-   - **Optimization Techniques**: Use **gradient checkpointing** to save memory during training. Use quantized model to reduce memory requirements and speed up computations.
-   - Employ `SFTTrainer` trainer from the TRL library, to streamline the training process.
+1. **Chuẩn bị dữ liệu**: Bắt đầu với tập dữ liệu được gán nhãn ghép nối hình ảnh với văn bản, chẳng hạn như câu hỏi và câu trả lời. Ví dụ: trong các tác vụ như phân tích biểu đồ, tập dữ liệu `HuggingFaceM4/ChartQA` bao gồm hình ảnh biểu đồ, truy vấn và câu trả lời ngắn gọn.
 
+2. **Thiết lập mô hình**: Tải VLM đã được huấn luyện trước phù hợp với nhiệm vụ, chẳng hạn như `HuggingFaceTB/SmolVLM-Instruct`, và một bộ xử lý (processor) để chuẩn bị đầu vào văn bản và hình ảnh. Điều chỉnh cấu hình của mô hình để phù hợp với phần cứng của bạn.
 
-## Preference Optimization
+3. **Quá trình tinh chỉnh**:
+   - **Định dạng dữ liệu**: Cấu trúc tập dữ liệu thành định dạng giống như chatbot, ghép nối các câu lệnh hệ thống (system messages), các truy vấn của người dùng và các câu trả lời tương ứng.
+   - **Cấu hình huấn luyện**: Sử dụng các công cụ như `TrainingArguments` của Hugging Face hoặc `SFTConfig` của TRL để thiết lập các tham số huấn luyện. Chúng bao gồm kích thước batch, tốc độ học và các bước tích lũy gradient để tối ưu hóa việc sử dụng tài nguyên.
+   - **Kỹ thuật tối ưu hóa**: Sử dụng **gradient checkpointing** để tiết kiệm bộ nhớ trong quá trình huấn luyện. Sử dụng mô hình đã lượng tử hóa để giảm yêu cầu bộ nhớ và tăng tốc độ tính toán.
+   - Sử dụng `SFTTrainer` từ thư viện TRL, để hợp lý hóa quá trình huấn luyện.
 
-Preference Optimization, particularly Direct Preference Optimization (DPO), trains a Vision Language Model (VLM) to align with human preferences. Instead of strictly following predefined instructions, the model learns to prioritize outputs that humans subjectively prefer. This approach is particularly useful for tasks involving creative judgment, nuanced reasoning, or varying acceptable answers.
+## Tối ưu hóa theo Sở thích (Preference Optimization)
 
+Tối ưu hóa theo Sở thích, đặc biệt là Tối ưu hóa Sở thích Trực tiếp (Direct Preference Optimization - DPO), huấn luyện Mô hình Ngôn ngữ Thị giác (VLM) để phù hợp với sở thích của con người. Thay vì tuân theo các hướng dẫn được xác định trước một cách nghiêm ngặt, mô hình học cách ưu tiên các đầu ra mà con người chủ quan thích hơn. Phương pháp này đặc biệt hữu ích cho các tác vụ liên quan đến phán đoán sáng tạo, lý luận sắc thái hoặc các câu trả lời có thể chấp nhận được khác nhau.
 
-### **Overview**
-Preference Optimization addresses scenarios where subjective human preferences are central to task success. By fine-tuning on datasets that encode human preferences, DPO enhances the model's ability to generate responses that are contextually and stylistically aligned with user expectations. This method is particularly effective for tasks like creative writing, customer interactions, or multi-choice scenarios.
+### **Tổng quan**
 
-Despite its benefits, Preference Optimization has challenges:
-- **Data Quality**: High-quality, preference-annotated datasets are required, often making data collection a bottleneck.
-- **Complexity**: Training can involve sophisticated processes such as pairwise sampling of preferences and balancing computational resources.
+Tối ưu hóa theo Sở thích giải quyết các tình huống trong đó sở thích chủ quan của con người là trung tâm của sự thành công của nhiệm vụ. Bằng cách tinh chỉnh trên các tập dữ liệu mã hóa sở thích của con người, DPO nâng cao khả năng của mô hình trong việc tạo ra các phản hồi phù hợp với ngữ cảnh và phong cách với mong đợi của người dùng. Phương pháp này đặc biệt hiệu quả cho các tác vụ như viết sáng tạo, tương tác với khách hàng hoặc các tình huống có nhiều lựa chọn.
 
-Preference datasets must capture clear preferences between candidate outputs. For example, a dataset may pair a question with two responses—one preferred and the other less acceptable. The model learns to predict the preferred response, even if it's not entirely correct, as long as it's better aligned with human judgment.
+Mặc dù có những lợi ích, Tối ưu hóa theo Sở thích có những thách thức:
 
+- **Chất lượng dữ liệu**: Cần có các tập dữ liệu được chú thích theo sở thích chất lượng cao, thường làm cho việc thu thập dữ liệu trở thành một nút thắt cổ chai.
+- **Độ phức tạp**: Việc huấn luyện có thể liên quan đến các quy trình phức tạp như lấy mẫu theo cặp các sở thích và cân bằng tài nguyên tính toán.
 
-### **Usage**
-1. **Dataset Preparation**  
-   A preference-labeled dataset is crucial for training. Each example typically consists of a prompt (e.g., an image and question) and two candidate responses: one chosen (preferred) and one rejected. For example:
+Các tập dữ liệu sở thích phải nắm bắt được các sở thích rõ ràng giữa các đầu ra ứng viên. Ví dụ: một tập dữ liệu có thể ghép nối một câu hỏi với hai câu trả lời—một câu trả lời được ưu tiên và câu trả lời kia ít được chấp nhận hơn. Mô hình học cách dự đoán câu trả lời được ưu tiên, ngay cả khi nó không hoàn toàn chính xác, miễn là nó phù hợp hơn với đánh giá của con người.
 
-   - **Question**: How many families?  
-     - **Rejected**: The image does not provide any information about families.  
-     - **Chosen**: The image shows a Union Organization table setup with 18,000 families.  
+### **Cách sử dụng**
 
-   The dataset teaches the model to prioritize better-aligned responses, even if they aren’t perfect. 
+1. **Chuẩn bị tập dữ liệu**
+   Một tập dữ liệu được gán nhãn sở thích là rất quan trọng để huấn luyện. Mỗi ví dụ thường bao gồm một lời nhắc (prompt) (ví dụ: một hình ảnh và câu hỏi) và hai câu trả lời ứng viên: một câu được chọn (ưa thích) và một câu bị từ chối. Ví dụ:
 
-2. **Model Setup**  
-   Load a pre-trained VLM and integrate it with Hugging Face's TRL library, which supports DPO, and a processor for preparing text and image inputs. Configure the model for supervised learning and suitability for your hardware.
+   - **Câu hỏi**: Có bao nhiêu gia đình?
+     - **Bị từ chối**: Hình ảnh không cung cấp bất kỳ thông tin nào về các gia đình.
+     - **Được chọn**: Hình ảnh cho thấy một bảng gồm 18.000 gia đình.
 
-3. **Training Pipeline**  
-   Training involves configuring DPO-specific parameters. Here's a summary of the process:
+   Tập dữ liệu dạy cho mô hình ưu tiên các câu trả lời phù hợp hơn, ngay cả khi chúng không hoàn hảo.
 
-   - **Format Dataset**: Structure each sample with prompts, images, and candidate answers.
-   - **Loss Function**: Use a preference-based loss function to optimize the model for selecting the preferred output.
-   - **Efficient Training**: Combine techniques like quantization, gradient accumulation, and LoRA adapters to optimize memory and computation.
+2. **Thiết lập mô hình**
+   Tải VLM đã được huấn luyện trước và tích hợp nó với thư viện TRL của Hugging Face, hỗ trợ DPO và bộ xử lý để chuẩn bị đầu vào văn bản và hình ảnh. Định cấu hình mô hình để học có giám sát và phù hợp với phần cứng của bạn.
 
+3. **Quy trình huấn luyện**
+   Việc huấn luyện bao gồm việc định cấu hình các tham số cụ thể cho DPO. Dưới đây là bản tóm tắt về quy trình:
 
+   - **Định dạng tập dữ liệu**: Cấu trúc từng mẫu với lời nhắc, hình ảnh và câu trả lời ứng viên.
+   - **Hàm mất mát (Loss Function)**: Sử dụng hàm mất mát dựa trên sở thích để tối ưu hóa mô hình để chọn đầu ra được ưu tiên.
+   - **Huấn luyện hiệu quả**: Kết hợp các kỹ thuật như lượng tử hóa, tích lũy gradient và bộ điều hợp LoRA (LoRA adapters) để tối ưu hóa bộ nhớ và tính toán.
 
-## Resources
+## Tài liệu tham khảo
 
-- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) 
-- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
-- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
-- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
+- [Hugging Face Learn: Tinh chỉnh có giám sát VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
+- [Hugging Face Learn: Tinh chỉnh có giám sát SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)
+- [Hugging Face Learn: Tinh chỉnh tối ưu hóa tùy chọn SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)
+- [Hugging Face Blog: Tối ưu hóa tùy chọn cho VLMs](https://huggingface.co/blog/dpo_vlm)
 
-## Next Steps
+## Các bước tiếp theo
 
-⏩ Try the [vlm_finetune_sample.ipynb](./notebooks/vlm_finetune_sample.ipynb) to implement this unified approach to preference alignment.
\ No newline at end of file
+⏩ Thử [vlm_finetune_sample.ipynb](./notebooks/vlm_finetune_sample.ipynb) để triển khai phương pháp thống nhất này để căn chỉnh tùy chọn.

From 09f3b2fff778f51c1a1231b10f2a1fab2c0b9dd3 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Fri, 17 Jan 2025 22:57:19 +0100
Subject: [PATCH 49/63] init

---
 vi/5_vision_language_models/vlm_usage.md | 70 ++++++++++++------------
 1 file changed, 35 insertions(+), 35 deletions(-)

diff --git a/vi/5_vision_language_models/vlm_usage.md b/vi/5_vision_language_models/vlm_usage.md
index ef812927..84b6348b 100644
--- a/vi/5_vision_language_models/vlm_usage.md
+++ b/vi/5_vision_language_models/vlm_usage.md
@@ -1,58 +1,58 @@
-# Visual Language Models
+# Mô hình Ngôn ngữ Thị giác (Visual Language Models)
 
-Visual Language Models (VLMs) bridge the gap between images and text, enabling advanced tasks like generating image captions, answering questions based on visuals, or understanding the relationship between textual and visual data. Their architecture is designed to process both modalities seamlessly.
+Mô hình Ngôn ngữ Thị giác (VLMs) thu hẹp khoảng cách giữa hình ảnh và văn bản, cho phép thực hiện các tác vụ nâng cao như tạo chú thích cho ảnh, trả lời câu hỏi dựa trên hình ảnh hoặc hiểu mối quan hệ giữa dữ liệu văn bản và hình ảnh. Kiến trúc của chúng được thiết kế để xử lý liền mạch cả hai phương thức.
 
-### Architecture
+### Kiến trúc
 
-VLMs combine image-processing components with text-generation models to achieve a unified understanding. The primary elements of their architecture are:
+VLMs kết hợp các thành phần xử lý hình ảnh với các mô hình sinh văn bản để đạt được sự hiểu biết thống nhất. Các yếu tố chính trong kiến trúc của chúng là:
 
-![VLM Architecture](./images/VLM_Architecture.png)
+![Kiến trúc VLM](./images/VLM_Architecture.png)
 
-- **Image Encoder**: Transforms raw images into compact numerical representations. Pretrained encoders such as CLIP or vision transformers (ViT) are commonly used.
-- **Embedding Projector**: Maps image features into a space compatible with textual embeddings, often using dense layers or linear transformations.
-- **Text Decoder**: Acts as the language-generation component, translating fused multimodal information into coherent text. Examples include generative models like Llama or Vicuna.
-- **Multimodal Projector**: Provides an additional layer to blend image and text representations. It is critical for models like LLaVA to establish stronger connections between the two modalities.
+- **Bộ mã hóa hình ảnh (Image Encoder)**: Biến đổi hình ảnh thô thành các biểu diễn số học nhỏ gọn. Các bộ mã hóa được huấn luyện trước như CLIP hoặc vision transformers (ViT) thường được sử dụng.
+- **Bộ chiếu nhúng (Embedding Projector)**: Ánh xạ các đặc trưng của hình ảnh vào không gian tương thích với các phép nhúng văn bản, thường sử dụng các lớp dày đặc (dense layers) hoặc các phép biến đổi tuyến tính.
+- **Bộ giải mã văn bản (Text Decoder)**: Hoạt động như thành phần sinh ngôn ngữ, chuyển thông tin đa phương thức đã hợp nhất thành văn bản mạch lạc. Ví dụ bao gồm các mô hình sinh (generative models) như Llama hoặc Vicuna.
+- **Bộ chiếu đa phương thức (Multimodal Projector)**: Cung cấp một lớp bổ sung để trộn lẫn các biểu diễn hình ảnh và văn bản. Nó rất quan trọng đối với các mô hình như LLaVA để thiết lập các kết nối mạnh mẽ hơn giữa hai phương thức.
 
-Most VLMs leverage pretrained image encoders and text decoders and align them through additional fine-tuning on paired image-text datasets. This approach makes training efficient while allowing the models to generalize effectively.
+Hầu hết các VLMs tận dụng các bộ mã hóa hình ảnh và bộ giải mã văn bản đã được huấn luyện trước và căn chỉnh chúng thông qua việc tinh chỉnh bổ sung trên các tập dữ liệu hình ảnh-văn bản được ghép nối. Cách tiếp cận này giúp việc huấn luyện hiệu quả đồng thời cho phép các mô hình khái quát hóa một cách hiệu quả.
 
-### Usage
+### Cách sử dụng
 
-![VLM Process](./images/VLM_Process.png)
+![Quy trình VLM](./images/VLM_Process.png)
 
-VLMs are applied to a range of multimodal tasks. Their adaptability allows them to perform in diverse domains with varying levels of fine-tuning:
+VLMs được áp dụng cho một loạt các tác vụ đa phương thức. Khả năng thích ứng của chúng cho phép chúng hoạt động trong các lĩnh vực đa dạng với các mức độ tinh chỉnh khác nhau:
 
-- **Image Captioning**: Generating descriptions for images.
-- **Visual Question Answering (VQA)**: Answering questions about the content of an image.
-- **Cross-Modal Retrieval**: Finding corresponding text for a given image or vice versa.
-- **Creative Applications**: Assisting in design, art generation, or creating engaging multimedia content.
+- **Chú thích hình ảnh (Image Captioning)**: Tạo mô tả cho hình ảnh.
+- **Trả lời câu hỏi bằng hình ảnh (Visual Question Answering - VQA)**: Trả lời câu hỏi về nội dung của hình ảnh.
+- **Truy xuất đa phương thức (Cross-Modal Retrieval)**: Tìm văn bản tương ứng cho một hình ảnh nhất định hoặc ngược lại.
+- **Ứng dụng sáng tạo (Creative Applications)**: Hỗ trợ thiết kế, tạo tác phẩm nghệ thuật hoặc tạo nội dung đa phương tiện hấp dẫn.
 
-![VLM Usage](./images/VLM_Usage.png)
+![Sử dụng VLM](./images/VLM_Usage.png)
 
-Training and fine-tuning VLMs depend on high-quality datasets that pair images with text annotations. Tools like Hugging Face's `transformers` library provide convenient access to pretrained VLMs and streamlined workflows for custom fine-tuning.
+Việc huấn luyện và tinh chỉnh VLMs phụ thuộc vào các tập dữ liệu chất lượng cao ghép nối hình ảnh với chú thích văn bản. Các công cụ như thư viện `transformers` của Hugging Face cung cấp quyền truy cập thuận tiện vào các VLMs đã được huấn luyện trước và quy trình làm việc được sắp xếp hợp lý để tinh chỉnh tùy chỉnh.
 
-### Chat Format
+### Định dạng trò chuyện (Chat Format)
 
-Many VLMs are structured to interact in a chatbot-like manner, enhancing usability. This format includes:
+Nhiều VLMs được cấu trúc để tương tác theo kiểu chatbot, nâng cao khả năng sử dụng. Định dạng này bao gồm:
 
-- A **system message** that sets the role or context for the model, such as "You are an assistant analyzing visual data."
-- **User queries** that combine text inputs and associated images.
-- **Assistant responses** that provide text outputs derived from the multimodal analysis.
+- Một **câu lệnh hệ thống (system message)** đặt vai trò hoặc ngữ cảnh cho mô hình, chẳng hạn như "Bạn là trợ lý phân tích dữ liệu hình ảnh."
+- **Truy vấn của người dùng (user queries)** kết hợp đầu vào văn bản và hình ảnh liên quan.
+- **Phản hồi của trợ lý (assistant responses)** cung cấp đầu ra văn bản bắt nguồn từ phân tích đa phương thức.
 
-This conversational structure is intuitive and aligns with user expectations, especially for interactive applications like customer service or educational tools.
+Cấu trúc hội thoại này trực quan và phù hợp với mong đợi của người dùng, đặc biệt là đối với các ứng dụng tương tác như dịch vụ khách hàng hoặc các công cụ giáo dục.
 
-Here’s an example of how a formatted input might look:
+Dưới đây là ví dụ về cách hiển thị đầu vào được định dạng:
 
 ```json
 [
     {
         "role": "system",
-        "content": [{"type": "text", "text": "You are a Vision Language Model specialized in interpreting visual data from chart images..."}]
+        "content": [{"type": "text", "text": "Bạn là một Mô hình Ngôn ngữ Thị giác chuyên giải thích dữ liệu hình ảnh từ hình ảnh biểu đồ..."}]
     },
     {
         "role": "user",
         "content": [
             {"type": "image", "image": "<image_data>"},
-            {"type": "text", "text": "What is the highest value in the bar chart?"}
+            {"type": "text", "text": "Giá trị cao nhất trong biểu đồ cột là bao nhiêu?"}
         ]
     },
     {
@@ -62,15 +62,15 @@ Here’s an example of how a formatted input might look:
 ]
 ```
 
-**Working with Multiple Images and Videos**
+**Làm việc với nhiều hình ảnh và video**
 
-VLMs can also process multiple images or even videos by adapting the input structure to accommodate sequential or parallel visual inputs. For videos, frames can be extracted and processed as individual images, while maintaining temporal order.
+VLMs cũng có thể xử lý nhiều hình ảnh hoặc thậm chí video bằng cách điều chỉnh cấu trúc đầu vào để chứa các đầu vào hình ảnh tuần tự hoặc song song. Đối với video, các khung hình có thể được trích xuất và xử lý như các hình ảnh riêng lẻ, trong khi vẫn duy trì thứ tự thời gian.
 
-## Resources
+## Tài liệu tham khảo
 
-- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
-- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm) 
+- [Hugging Face Blog: Mô hình Ngôn ngữ Thị giác](https://huggingface.co/blog/vlms)
+- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)
 
-## Next Steps
+## Các bước tiếp theo
 
-⏩ Try the [vlm_usage_sample.ipynb](./notebooks/vlm_usage_sample.ipynb) to try different usages of SMOLVLM.
\ No newline at end of file
+⏩ Thử [vlm_usage_sample.ipynb](./notebooks/vlm_usage_sample.ipynb) để thử các cách sử dụng khác nhau của SMOLVLM.

From aee7329b541d1fe90beb7ca61e643889a56c6e76 Mon Sep 17 00:00:00 2001
From: Ads Dawson <104169244+GangGreenTemperTatum@users.noreply.github.com>
Date: Sat, 18 Jan 2025 16:37:53 -0500
Subject: [PATCH 50/63] fix: notebook import error

---
 8_agents/notebooks/agents.ipynb | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/8_agents/notebooks/agents.ipynb b/8_agents/notebooks/agents.ipynb
index 02d278c6..1f197346 100644
--- a/8_agents/notebooks/agents.ipynb
+++ b/8_agents/notebooks/agents.ipynb
@@ -146,8 +146,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "from smolagents import CodeAgent\n",
-                "from smolagents.tools import DuckDuckGoSearch\n",
+                "from smolagents import CodeAgent, DuckDuckGoSearchTool\n",
                 "\n",
                 "# Initialize the agent with memory\n",
                 "research_agent = CodeAgent(...)  # TODO: Define the agent\n",
@@ -178,6 +177,18 @@
             "display_name": "Python 3",
             "language": "python",
             "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.12.8"
         }
     },
     "nbformat": 4,

From 7d51209f0f34de9c7b6054973858e7d77fdab7df Mon Sep 17 00:00:00 2001
From: Ikko Eltociear Ashimine <eltociear@gmail.com>
Date: Mon, 20 Jan 2025 15:46:04 +0900
Subject: [PATCH 51/63] [TRANSLATION] Add 5_vision_language_models and
 6_synthetic_datasets EN-JA translation

---
 ja/5_vision_language_models/README.md         |  37 ++
 .../notebooks/vlm_sft_sample.ipynb            | 469 ++++++++++++++++++
 .../notebooks/vlm_usage_sample.ipynb          | 469 ++++++++++++++++++
 ja/5_vision_language_models/vlm_finetuning.md |  96 ++++
 ja/5_vision_language_models/vlm_usage.md      |  76 +++
 ja/6_synthetic_datasets/README.md             |  39 ++
 .../instruction_datasets.md                   | 197 ++++++++
 .../notebooks/instruction_sft_dataset.ipynb   | 119 +++++
 .../notebooks/preference_dpo_dataset.ipynb    | 120 +++++
 .../preference_datasets.md                    | 155 ++++++
 10 files changed, 1777 insertions(+)
 create mode 100644 ja/5_vision_language_models/README.md
 create mode 100644 ja/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
 create mode 100644 ja/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
 create mode 100644 ja/5_vision_language_models/vlm_finetuning.md
 create mode 100644 ja/5_vision_language_models/vlm_usage.md
 create mode 100644 ja/6_synthetic_datasets/README.md
 create mode 100644 ja/6_synthetic_datasets/instruction_datasets.md
 create mode 100644 ja/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
 create mode 100644 ja/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
 create mode 100644 ja/6_synthetic_datasets/preference_datasets.md

diff --git a/ja/5_vision_language_models/README.md b/ja/5_vision_language_models/README.md
new file mode 100644
index 00000000..9fac3c40
--- /dev/null
+++ b/ja/5_vision_language_models/README.md
@@ -0,0 +1,37 @@
+# ビジョン言語モデル
+
+## 1. VLMの使用
+
+ビジョン言語モデル（VLM）は、画像キャプション生成、視覚質問応答、マルチモーダル推論などのタスクを可能にするために、テキストと並行して画像入力を処理します。
+
+典型的なVLMアーキテクチャは、視覚的特徴を抽出する画像エンコーダ、視覚的およびテキスト表現を整列させるプロジェクション層、およびテキストを処理または生成する言語モデルで構成されます。これにより、モデルは視覚要素と言語概念の間の接続を確立できます。
+
+VLMは、使用ケースに応じてさまざまな構成で使用できます。ベースモデルは一般的なビジョン言語タスクを処理し、チャット最適化されたバリアントは会話型インタラクションをサポートします。一部のモデルには、視覚的証拠に基づいて予測を行うための追加コンポーネントや、物体検出などの特定のタスクに特化したコンポーネントが含まれています。
+
+VLMの技術的な詳細と使用方法については、[VLMの使用](./vlm_usage.md)ページを参照してください。
+
+## 2. VLMのファインチューニング
+
+VLMのファインチューニングは、特定のタスクを実行するため、または特定のデータセットで効果的に動作するように、事前トレーニングされたモデルを適応させるプロセスです。このプロセスは、モジュール1および2で紹介されたように、教師ありファインチューニング、好みの最適化、またはその両方を組み合わせたハイブリッドアプローチなどの方法論に従うことができます。
+
+コアツールと技術はLLMで使用されるものと似ていますが、VLMのファインチューニングには、画像のデータ表現と準備に特に注意を払う必要があります。これにより、モデルが視覚データとテキストデータの両方を効果的に統合および処理し、最適なパフォーマンスを発揮できるようになります。デモモデルであるSmolVLMは、前のモジュールで使用された言語モデルよりも大幅に大きいため、効率的なファインチューニング方法を探ることが重要です。量子化やPEFTなどの技術を使用することで、プロセスをよりアクセスしやすく、コスト効果の高いものにし、より多くのユーザーがモデルを試すことができます。
+
+VLMのファインチューニングに関する詳細なガイダンスについては、[VLMのファインチューニング](./vlm_finetuning.md)ページを参照してください。
+
+## 演習ノートブック
+
+| タイトル | 説明 | 演習 | リンク | Colab |
+|-------|-------------|----------|------|-------|
+| VLMの使用 | 事前トレーニングされたVLMをさまざまなタスクに使用する方法を学ぶ | 🐢 画像を処理する<br>🐕 バッチ処理で複数の画像を処理する<br>🦁 ビデオ全体を処理する | [ノートブック](./notebooks/vlm_usage_sample.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_usage_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+| VLMのファインチューニング | タスク固有のデータセットに対して事前トレーニングされたVLMをファインチューニングする方法を学ぶ | 🐢 基本的なデータセットを使用してファインチューニングする<br>🐕 新しいデータセットを試す<br>🦁 代替のファインチューニング方法を試す | [ノートブック](./notebooks/vlm_sft_sample.ipynb) | <a target="_blank" href="https://colab.research.google.com/github/huggingface/smol-course/blob/main/5_vision_language_models/notebooks/vlm_sft_sample.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
+
+## 参考文献
+- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl)
+- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)
+- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)
+- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
+- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
+- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)
+- [Hugging Face Model: SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct)
+- [CLIP: Learning Transferable Visual Models from Natural Language Supervision](https://arxiv.org/abs/2103.00020)
+- [Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://arxiv.org/abs/2107.07651)
diff --git a/ja/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/ja/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
new file mode 100644
index 00000000..56ad5d65
--- /dev/null
+++ b/ja/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 画像とテキストをVLMで処理する\n",
+    "\n",
+    "このノートブックでは、`HuggingFaceTB/SmolVLM-Instruct` 4bit量子化モデルを使用して、以下のようなさまざまなマルチモーダルタスクを実行する方法を示します：\n",
+    "- 視覚質問応答（VQA）：画像の内容に基づいて質問に答える。\n",
+    "- テキスト認識（OCR）：画像内のテキストを抽出して解釈する。\n",
+    "- ビデオ理解：連続フレーム解析を通じてビデオを説明する。\n",
+    "\n",
+    "プロンプトを効果的に構築することで、シーン理解、ドキュメント分析、動的視覚推論など、多くのアプリケーションにモデルを活用できます。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件をインストール\n",
+    "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
+    "\n",
+    "# Hugging Faceに認証\n",
+    "from huggingface_hub import notebook_login\n",
+    "notebook_login()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
+      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
+      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'longest_edge': 1536}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch, PIL\n",
+    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
+    "from transformers.image_utils import load_image\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
+    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
+    "model = AutoModelForVision2Seq.from_pretrained(\n",
+    "    model_name,\n",
+    "    quantization_config=quantization_config,\n",
+    ").to(device)\n",
+    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
+    "\n",
+    "print(processor.image_processor.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 画像の処理"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "まず、画像のキャプションを生成し、画像に関する質問に答えることから始めましょう。また、複数の画像を処理する方法も探ります。\n",
+    "### 1. 単一画像のキャプション生成"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "image_url1 = \"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"\n",
+    "display(Image(url=image_url1))\n",
+    "\n",
+    "image_url2 = \"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"\n",
+    "display(Image(url=image_url2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>Can you describe the image?\\nAssistant: The image is a scene of a person walking in a forest. The person is wearing a coat and a cap. The person is holding the hand of another person. The person is walking on a path. The path is covered with dry leaves. The background of the image is a forest with trees.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 画像を1枚読み込む\n",
+    "image1 = load_image(image_url1)\n",
+    "\n",
+    "# 入力メッセージを作成\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"Can you describe the image?\"}\n",
+    "        ]\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# 入力を準備\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# 出力を生成\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. 複数画像の比較\n",
+    "モデルは複数の画像を処理して比較することもできます。2つの画像の共通のテーマを判断してみましょう。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# 画像を読み込む\n",
+    "image2 = load_image(image_url2)\n",
+    "\n",
+    "# 入力メッセージを作成\n",
+    "messages = [\n",
+    "    # {\n",
+    "    #     \"role\": \"user\",\n",
+    "    #     \"content\": [\n",
+    "    #         {\"type\": \"image\"},\n",
+    "    #         {\"type\": \"image\"},\n",
+    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
+    "    #     ]\n",
+    "    # },\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
+    "        ]\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# 入力を準備\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# 出力を生成\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 🔠 テキスト認識（OCR）\n",
+    "VLMは画像内のテキストを認識して解釈することもでき、ドキュメント分析などのタスクに適しています。\n",
+    "テキストが密集している画像で実験してみてください。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
+     ]
+    }
+   ],
+   "source": [
+    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
+    "display(Image(url=document_image_url))\n",
+    "\n",
+    "# ドキュメント画像を読み込む\n",
+    "document_image = load_image(document_image_url)\n",
+    "\n",
+    "# 分析のための入力メッセージを作成\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# 入力を準備\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# 出力を生成\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ビデオの処理\n",
+    "\n",
+    "視覚言語モデル（VLM）は、キーフレームを抽出し、時間順に推論することで間接的にビデオを処理できます。VLMは専用のビデオモデルの時間的モデリング機能を欠いていますが、それでも以下のことが可能です：\n",
+    "- サンプリングされたフレームを順次分析することで、アクションやイベントを説明する。\n",
+    "- 代表的なキーフレームに基づいてビデオに関する質問に答える。\n",
+    "- 複数のフレームのテキスト記述を組み合わせてビデオの内容を要約する。\n",
+    "\n",
+    "例を使って実験してみましょう：\n",
+    "\n",
+    "<video width=\"640\" height=\"360\" controls>\n",
+    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
+    "  Your browser does not support the video tag.\n",
+    "</video>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install opencv-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Video\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "\n",
+    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
+    "    cap = cv2.VideoCapture(video_path)\n",
+    "    if not cap.isOpened():\n",
+    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
+    "    \n",
+    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
+    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
+    "\n",
+    "    frames = []\n",
+    "    for idx in frame_indices:\n",
+    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
+    "        ret, frame = cap.read()\n",
+    "        if ret:\n",
+    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
+    "            if target_size:\n",
+    "                frames.append(resize_and_crop(frame, target_size))\n",
+    "            else:\n",
+    "                frames.append(frame)\n",
+    "    cap.release()\n",
+    "    return frames\n",
+    "\n",
+    "def resize_and_crop(image, target_size):\n",
+    "    width, height = image.size\n",
+    "    scale = target_size / min(width, height)\n",
+    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
+    "    left = (image.width - target_size) // 2\n",
+    "    top = (image.height - target_size) // 2\n",
+    "    return image.crop((left, top, left + target_size, top + target_size))\n",
+    "\n",
+    "# ビデオリンク\n",
+    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
+      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"Describe what the woman is doing.\"\n",
+    "\n",
+    "def generate_response(model, processor, frames, question):\n",
+    "\n",
+    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
+    "    messages = [\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
+    "        }\n",
+    "    ]\n",
+    "    inputs = processor(\n",
+    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
+    "        images=frames,\n",
+    "        return_tensors=\"pt\"\n",
+    "    ).to(model.device)\n",
+    "\n",
+    "    outputs = model.generate(\n",
+    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
+    "    )\n",
+    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
+    "\n",
+    "\n",
+    "# ビデオからフレームを抽出\n",
+    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
+    "\n",
+    "processor.image_processor.size = (384, 384)\n",
+    "processor.image_processor.do_resize = False\n",
+    "# 応答を生成\n",
+    "response = generate_response(model, processor, frames, question)\n",
+    "\n",
+    "# 結果を表示\n",
+    "# print(\"Question:\", question)\n",
+    "print(\"Response:\", response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完了です！\n",
+    "\n",
+    "このノートブックでは、マルチモーダルタスクのプロンプトをフォーマットする方法など、視覚言語モデル（VLM）を使用する方法を示しました。ここで説明した手順に従うことで、VLMとそのアプリケーションを実験できます。\n",
+    "\n",
+    "### 次のステップ\n",
+    "- VLMのさらなる使用例を試してみてください。\n",
+    "- 同僚と協力して、彼らのプルリクエスト（PR）をレビューしてください。\n",
+    "- 新しい使用例、例、概念を導入するために、問題を開いたりPRを提出したりして、このコース資料の改善に貢献してください。\n",
+    "\n",
+    "楽しい探求を！🌟"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ja/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/ja/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
new file mode 100644
index 00000000..abdaf7fd
--- /dev/null
+++ b/ja/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 画像とテキストをVLMで処理する\n",
+    "\n",
+    "このノートブックでは、`HuggingFaceTB/SmolVLM-Instruct` 4bit量子化モデルを使用して、以下のようなさまざまなマルチモーダルタスクを実行する方法を示します：\n",
+    "- 視覚質問応答（VQA）：画像の内容に基づいて質問に答える。\n",
+    "- テキスト認識（OCR）：画像内のテキストを抽出して解釈する。\n",
+    "- ビデオ理解：連続フレーム解析を通じてビデオを説明する。\n",
+    "\n",
+    "プロンプトを効果的に構築することで、シーン理解、ドキュメント分析、動的視覚推論など、多くのアプリケーションにモデルを活用できます。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Google Colabでの要件をインストール\n",
+    "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
+    "\n",
+    "# Hugging Faceに認証\n",
+    "from huggingface_hub import notebook_login\n",
+    "notebook_login()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
+      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
+      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'longest_edge': 1536}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch, PIL\n",
+    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
+    "from transformers.image_utils import load_image\n",
+    "\n",
+    "device = (\n",
+    "    \"cuda\"\n",
+    "    if torch.cuda.is_available()\n",
+    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+    ")\n",
+    "\n",
+    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
+    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
+    "model = AutoModelForVision2Seq.from_pretrained(\n",
+    "    model_name,\n",
+    "    quantization_config=quantization_config,\n",
+    ").to(device)\n",
+    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
+    "\n",
+    "print(processor.image_processor.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 画像の処理"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "まず、画像のキャプションを生成し、画像に関する質問に答えることから始めましょう。また、複数の画像を処理する方法も探ります。\n",
+    "### 1. 単一画像のキャプション生成"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "image_url1 = \"https://cdn.pixabay.com/photo/2024/11/20/09/14/christmas-9210799_1280.jpg\"\n",
+    "display(Image(url=image_url1))\n",
+    "\n",
+    "image_url2 = \"https://cdn.pixabay.com/photo/2024/11/23/08/18/christmas-9218404_1280.jpg\"\n",
+    "display(Image(url=image_url2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:451: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>Can you describe the image?\\nAssistant: The image is a scene of a person walking in a forest. The person is wearing a coat and a cap. The person is holding the hand of another person. The person is walking on a path. The path is covered with dry leaves. The background of the image is a forest with trees.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 画像を1枚読み込む\n",
+    "image1 = load_image(image_url1)\n",
+    "\n",
+    "# 入力メッセージを作成\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"Can you describe the image?\"}\n",
+    "        ]\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# 入力を準備\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# 出力を生成\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. 複数画像の比較\n",
+    "モデルは複数の画像を処理して比較することもできます。2つの画像の共通のテーマを判断してみましょう。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>What event do they both represent?\\nAssistant: Christmas.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# 画像を読み込む\n",
+    "image2 = load_image(image_url2)\n",
+    "\n",
+    "# 入力メッセージを作成\n",
+    "messages = [\n",
+    "    # {\n",
+    "    #     \"role\": \"user\",\n",
+    "    #     \"content\": [\n",
+    "    #         {\"type\": \"image\"},\n",
+    "    #         {\"type\": \"image\"},\n",
+    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
+    "    #     ]\n",
+    "    # },\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
+    "        ]\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# 入力を準備\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# 出力を生成\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 🔠 テキスト認識（OCR）\n",
+    "VLMは画像内のテキストを認識して解釈することもでき、ドキュメント分析などのタスクに適しています。\n",
+    "テキストが密集している画像で実験してみてください。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['User:<image>What is written?\\nAssistant: MERRY CHRISTMAS AND A HAPPY NEW YEAR']\n"
+     ]
+    }
+   ],
+   "source": [
+    "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
+    "display(Image(url=document_image_url))\n",
+    "\n",
+    "# ドキュメント画像を読み込む\n",
+    "document_image = load_image(document_image_url)\n",
+    "\n",
+    "# 分析のための入力メッセージを作成\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"image\"},\n",
+    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# 入力を準備\n",
+    "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
+    "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
+    "inputs = inputs.to(device)\n",
+    "\n",
+    "# 出力を生成\n",
+    "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
+    "generated_texts = processor.batch_decode(\n",
+    "    generated_ids,\n",
+    "    skip_special_tokens=True,\n",
+    ")\n",
+    "\n",
+    "print(generated_texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ビデオの処理\n",
+    "\n",
+    "視覚言語モデル（VLM）は、キーフレームを抽出し、時間順に推���することで間接的にビデオを処理できます。VLMは専用のビデオモデルの時間的モデリング機能を欠いていますが、それでも以下のことが可能です：\n",
+    "- サンプリングされたフレームを順次分析することで、アクションやイベントを説明する。\n",
+    "- 代表的なキーフレームに基づいてビデオに関する質問に答える。\n",
+    "- 複数のフレームのテキスト記述を組み合わせてビデオの内容を要約する。\n",
+    "\n",
+    "例を使って実験してみましょう：\n",
+    "\n",
+    "<video width=\"640\" height=\"360\" controls>\n",
+    "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
+    "  Your browser does not support the video tag.\n",
+    "</video>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install opencv-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Video\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "\n",
+    "def extract_frames(video_path, max_frames=50, target_size=None):\n",
+    "    cap = cv2.VideoCapture(video_path)\n",
+    "    if not cap.isOpened():\n",
+    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
+    "    \n",
+    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
+    "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
+    "\n",
+    "    frames = []\n",
+    "    for idx in frame_indices:\n",
+    "        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)\n",
+    "        ret, frame = cap.read()\n",
+    "        if ret:\n",
+    "            frame = PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
+    "            if target_size:\n",
+    "                frames.append(resize_and_crop(frame, target_size))\n",
+    "            else:\n",
+    "                frames.append(frame)\n",
+    "    cap.release()\n",
+    "    return frames\n",
+    "\n",
+    "def resize_and_crop(image, target_size):\n",
+    "    width, height = image.size\n",
+    "    scale = target_size / min(width, height)\n",
+    "    image = image.resize((int(width * scale), int(height * scale)), PIL.Image.Resampling.LANCZOS)\n",
+    "    left = (image.width - target_size) // 2\n",
+    "    top = (image.height - target_size) // 2\n",
+    "    return image.crop((left, top, left + target_size, top + target_size))\n",
+    "\n",
+    "# ビデオリンク\n",
+    "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Response: User: Following are the frames of a video in temporal order.<image>Describe what the woman is doing.\n",
+      "Assistant: The woman is hanging an ornament on a Christmas tree.\n"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"Describe what the woman is doing.\"\n",
+    "\n",
+    "def generate_response(model, processor, frames, question):\n",
+    "\n",
+    "    image_tokens = [{\"type\": \"image\"} for _ in frames]\n",
+    "    messages = [\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
+    "        }\n",
+    "    ]\n",
+    "    inputs = processor(\n",
+    "        text=processor.apply_chat_template(messages, add_generation_prompt=True),\n",
+    "        images=frames,\n",
+    "        return_tensors=\"pt\"\n",
+    "    ).to(model.device)\n",
+    "\n",
+    "    outputs = model.generate(\n",
+    "        **inputs, max_new_tokens=100, num_beams=5, temperature=0.7, do_sample=True, use_cache=True\n",
+    "    )\n",
+    "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
+    "\n",
+    "\n",
+    "# ビデオからフレームを抽出\n",
+    "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
+    "\n",
+    "processor.image_processor.size = (384, 384)\n",
+    "processor.image_processor.do_resize = False\n",
+    "# 応答を生成\n",
+    "response = generate_response(model, processor, frames, question)\n",
+    "\n",
+    "# 結果を表示\n",
+    "# print(\"Question:\", question)\n",
+    "print(\"Response:\", response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💐 完了です！\n",
+    "\n",
+    "このノートブックでは、マルチモーダルタスクのプロンプトをフォーマットする方法など、視覚言語モデル（VLM）を使用する方法を示しました。ここで説明した手順に従うことで、VLMとそのアプリケーションを実験できます。\n",
+    "\n",
+    "### 次のステップ\n",
+    "- VLMのさらなる使用例を試してみてください。\n",
+    "- 同僚と協力して、彼らのプルリクエスト（PR）をレビューしてください。\n",
+    "- 新しい使用例、例、概念を導入するために、問題を開いたりPRを提出したりして、このコース資料の改善に貢献してください。\n",
+    "\n",
+    "楽しい探求を！🌟"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ja/5_vision_language_models/vlm_finetuning.md b/ja/5_vision_language_models/vlm_finetuning.md
new file mode 100644
index 00000000..9f69d14b
--- /dev/null
+++ b/ja/5_vision_language_models/vlm_finetuning.md
@@ -0,0 +1,96 @@
+# VLMファインチューニング
+## 効率的なファインチューニング
+
+### 量子化
+量子化は、モデルの重みとアクティベーションの精度を低下させ、メモリ使用量を大幅に削減し、計算速度を向上させます。たとえば、`float32`から`bfloat16`に切り替えると、パラメータごとのメモリ要件が半分になり、パフォーマンスを維持できます。より積極的な圧縮のために、8ビットおよび4ビットの量子化を使用してメモリ使用量をさらに削減できますが、精度が若干低下する可能性があります。これらの技術は、モデルとオプティマイザの設定の両方に適用でき、リソースが限られたハードウェアでの効率的なトレーニングを可能にします。
+
+### PEFT & LoRA
+モジュール3で紹介されたように、LoRA（Low-Rank Adaptation）は、元のモデルの重みを固定したまま、コンパクトなランク分解行列を学習することに焦点を当てています。これにより、トレーニング可能なパラメータの数が大幅に削減され、リソース要件が大幅に削減されます。LoRAはPEFTと統合されることで、大規模モデルのファインチューニングを可能にし、トレーニング可能なパラメータの小さなサブセットのみを調整します。このアプローチは、タスク固有の適応に特に効果的であり、数十億のトレーニング可能なパラメータを数百万に削減しながらパフォーマンスを維持します。
+
+### バッチサイズの最適化
+ファインチューニングのバッチサイズを最適化するには、大きな値から始め、メモリ不足（OOM）エラーが発生した場合は減らします。`gradient_accumulation_steps`を増やして補い、複数の更新にわたって合計バッチサイズを実質的に維持します。さらに、`gradient_checkpointing`を有効にして、逆伝播中に中間状態を再計算することでメモリ使用量を削減し、計算時間を犠牲にしてアクティベーションメモリ要件を削減します。これらの戦略は、ハードウェアの利用を最大化し、メモリ制約を克服するのに役立ちます。
+
+```python
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(
+    output_dir="./fine_tuned_model",  # モデルチェックポイントのディレクトリ
+    per_device_train_batch_size=4,   # デバイスごとのバッチサイズ（GPU/TPU）
+    num_train_epochs=3,              # トレーニングの総エポック数
+    learning_rate=5e-5,              # 学習率
+    save_steps=1000,                 # 1000ステップごとにチェックポイントを保存
+    bf16=True,                       # トレーニングに混合精度を使用
+    gradient_checkpointing=True,     # アクティベーションメモリ使用量を削減するために有効にする
+    gradient_accumulation_steps=16,  # 16ステップにわたって勾配を蓄積
+    logging_steps=50                 # 50ステップごとにメトリクスをログ
+)
+```
+
+## **教師ありファインチューニング（SFT）**
+
+教師ありファインチューニング（SFT）は、事前トレーニングされたビジョン言語モデル（VLM）を特定のタスクに適応させるために、画像と対応するテキストを含むラベル付きデータセットを活用するプロセスです。この方法は、視覚質問応答、画像キャプション生成、チャート解釈などのドメイン固有またはタスク固有の機能を実行するモデルの能力を向上させます。
+
+### **概要**
+SFTは、ベースモデルの一般的な能力が不足する場合に、特定のドメインや特定の問題を解決するためにVLMを専門化する必要がある場合に不可欠です。たとえば、モデルが独自の視覚的特徴やドメイン固有の用語に苦労する場合、SFTはラベル付きデータから学習することでこれらの領域に焦点を当てることができます。
+
+SFTは非常に効果的ですが、いくつかの制限があります：
+- **データ依存性**：タスクに合わせた高品質のラベル付きデータセットが必要です。
+- **計算リソース**：大規模なVLMのファインチューニングはリソース集約的です。
+- **過学習のリスク**：モデルがあまりにも狭くファインチューニングされると、一般化能力を失う可能性があります。
+
+これらの課題にもかかわらず、SFTは特定のコンテキストでのモデルパフォーマンスを向上させるための強力な手法です。
+
+### **使用方法**
+1. **データ準備**：画像とテキストのペアを含むラベル付きデータセットから始めます。たとえば、チャート分析のタスクでは、`HuggingFaceM4/ChartQA`データセットにはチャート画像、クエリ、および簡潔な応答が含まれています。
+
+2. **モデル設定**：タスクに適した事前トレーニングされたVLM（例：`HuggingFaceTB/SmolVLM-Instruct`）と、テキストと画像の入力を準備するためのプロセッサをロードします。モデルを教師あり学習に設定し、ハードウェアに適した設定を行います。
+
+3. **ファインチューニングプロセス**：
+   - **データのフォーマット**：データセットをチャットボットのような形式に構造化し、システムメッセージ、ユーザークエリ、および対応する回答をペアにします。
+   - **トレーニング設定**：Hugging Faceの`TrainingArguments`やTRLの`SFTConfig`などのツールを使用してトレーニングパラメータを設定します。これには、バッチサイズ、学習率、およびリソース使用を最適化するための勾配蓄積ステップが含まれます。
+   - **最適化技術**：トレーニング中のメモリを節約するために**gradient checkpointing**を使用します。メモリ要件を削減し、計算速度を向上させるために量子化モデルを使用します。
+   - TRLライブラリの`SFTTrainer`トレーナーを使用して、トレーニングプロセスを簡素化します。
+
+## 好みの最適化
+
+好みの最適化、特に直接好みの最適化（DPO）は、ビジョン言語モデル（VLM）を人間の好みに合わせるためのトレーニング手法です。事前定義された指示に厳密に従うのではなく、モデルは人間が主観的に好む出力を優先することを学習します。このアプローチは、創造的な判断、微妙な推論、または許容される回答が異なるタスクに特に有用です。
+
+### **概要**
+好みの最適化は、主観的な人間の好みがタスクの成功にとって重要なシナリオに対処します。好みをエンコードしたデータセットでファインチューニングすることで、DPOはモデルの応答生成能力を強化し、ユーザーの期待に文脈的およびスタイル的に一致させます。この方法は、創造的な執筆、顧客との対話、または複数の選択肢があるシナリオなどのタスクに特に効果的です。
+
+その利点にもかかわらず、好みの最適化にはいくつかの課題があります：
+- **データ品質**：高品質の好みが注釈されたデータセットが必要であり、データ収集がボトルネックになることがあります。
+- **複雑さ**：トレーニングには、好みのペアワイズサンプリングや計算リソースのバランスなどの高度なプロセスが含まれることがあります。
+
+好みのデータセットは、候補出力間の明確な好みをキャプチャする必要があります。たとえば、データセットは質問と2つの応答（1つは好まれ、もう1つは受け入れられない）をペアにすることがあります。モデルは、完全に正しくなくても、より人間の判断に一致する好ましい応答を予測することを学習します。
+
+### **使用方法**
+1. **データセットの準備**  
+   トレーニングには、好みがラベル付けされたデータセットが重要です。各例は通常、プロンプト（例：画像と質問）と2つの候補応答（1つは選択され、もう1つは拒否される）で構成されます。たとえば：
+
+   - **質問**：家族は何人ですか？  
+     - **拒否**：画像には家族に関する情報はありません。  
+     - **選択**：画像には18,000家族のユニオン組織のテーブル設定が表示されています。  
+
+   データセットは、モデルに完全に正しくなくても、より人間の判断に一致する好ましい応答を優先することを教えます。
+
+2. **モデル設定**  
+   事前トレーニングされたVLMをロードし、DPOをサポートするHugging FaceのTRLライブラリと統合し、テキストと画像の入力を準備するためのプロセッサを使用します。モデルを教師あり学習に設定し、ハードウェアに適した設定を行います。
+
+3. **トレーニングパイプライン**  
+   トレーニングには、DPO固有のパラメータの設定が含まれます。プロセスの概要は次のとおりです：
+
+   - **データセットのフォーマット**：各サンプルをプロンプト、画像、および候補応答で構成します。
+   - **損失関数**：好みに基づく損失関数を使用して、好ましい出力を選択するようにモデルを最適化します。
+   - **効率的なトレーニング**：量子化、勾配蓄積、LoRAアダプタなどの技術を組み合わせて、メモリと計算を最適化します。
+
+## リソース
+
+- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) 
+- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl)  
+- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct)  
+- [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm)
+
+## 次のステップ
+
+⏩ この統一アプローチを好みの最適化に実装するには、[vlm_finetune_sample.ipynb](./notebooks/vlm_finetune_sample.ipynb)を試してください。
diff --git a/ja/5_vision_language_models/vlm_usage.md b/ja/5_vision_language_models/vlm_usage.md
new file mode 100644
index 00000000..35f5ac85
--- /dev/null
+++ b/ja/5_vision_language_models/vlm_usage.md
@@ -0,0 +1,76 @@
+# ビジュアル言語モデル
+
+ビジュアル言語モデル（VLM）は、画像とテキストの間のギャップを埋め、画像キャプションの生成、視覚に基づく質問への回答、テキストと視覚データの関係の理解などの高度なタスクを可能にします。そのアーキテクチャは、両方のモダリティをシームレスに処理するように設計されています。
+
+### アーキテクチャ
+
+VLMは、画像処理コンポーネントとテキスト生成モデルを組み合わせて、統一された理解を実現します。主な要素は次のとおりです：
+
+![VLMアーキテクチャ](./images/VLM_Architecture.png)
+
+- **画像エンコーダ**：生の画像をコンパクトな数値表現に変換します。CLIPやビジョントランスフォーマー（ViT）などの事前トレーニングされたエンコーダが一般的に使用されます。
+- **埋め込みプロジェクタ**：画像特徴をテキスト埋め込みと互換性のある空間にマッピングします。通常、密な層や線形変換を使用します。
+- **テキストデコーダ**：マルチモーダル情報を翻訳して一貫したテキストを生成する言語生成コンポーネントです。LlamaやVicunaなどの生成モデルが例として挙げられます。
+- **マルチモーダルプロジェクタ**：画像とテキストの表現をブレンドするための追加の層を提供します。LLaVAのようなモデルでは、2つのモダリティ間の強力な接続を確立するために重要です。
+
+ほとんどのVLMは、事前トレーニングされた画像エンコーダとテキストデコーダを活用し、ペアになった画像テキストデータセットで追加のファインチューニングを行います。このアプローチにより、効率的なトレーニングが可能になり、モデルが効果的に一般化できるようになります。
+
+### 使用方法
+
+![VLMプロセス](./images/VLM_Process.png)
+
+VLMは、さまざまなマルチモーダルタスクに適用されます。その適応性により、さまざまなドメインで異なるレベルのファインチューニングを行うことができます：
+
+- **画像キャプション生成**：画像の説明を生成します。
+- **視覚質問応答（VQA）**：画像の内容に関する質問に答えます。
+- **クロスモーダル検索**：特定の画像に対応するテキストを見つける、またはその逆を行います。
+- **クリエイティブなアプリケーション**：デザイン支援、アート生成、魅力的なマルチメディアコンテンツの作成などに役立ちます。
+
+![VLMの使用](./images/VLM_Usage.png)
+
+VLMのトレーニングとファインチューニングには、画像とテキストの注釈がペアになった高品質なデータセットが必要です。Hugging Faceの`transformers`ライブラリなどのツールを使用すると、事前トレーニングされたVLMに簡単にアクセスでき、カスタムファインチューニングのワークフローが簡素化されます。
+
+### チャット形式
+
+多くのVLMは、使いやすさを向上させるためにチャットボットのような形式で構造化されています。この形式には次の要素が含まれます：
+
+- モデルの役割やコンテキストを設定する**システムメッセージ**（例：「あなたは視覚データを分析するアシスタントです」）。
+- テキスト入力と関連する画像を組み合わせた**ユーザークエリ**。
+- マルチモーダル分析から得られたテキスト出力を提供する**アシスタントの応答**。
+
+この会話形式は直感的であり、特にカスタマーサービスや教育ツールなどのインタラクティブなアプリケーションにおいて、ユーザーの期待に沿ったものです。
+
+フォーマットされた入力の例を次に示します：
+
+```json
+[
+    {
+        "role": "system",
+        "content": [{"type": "text", "text": "あなたはチャート画像の視覚データを解釈する専門のビジュアル言語モデルです..."}]
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "<image_data>"},
+            {"type": "text", "text": "棒グラフの最高値は何ですか？"}
+        ]
+    },
+    {
+        "role": "assistant",
+        "content": [{"type": "text", "text": "42"}]
+    }
+]
+```
+
+**複数の画像やビデオの処理**
+
+VLMは、入力構造を適応させることで、複数の画像やビデオを処理することもできます。ビデオの場合、フレームを抽出して個々の画像として処理し、時間順序を維持します。
+
+## リソース
+
+- [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms)
+- [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm)
+
+## 次のステップ
+
+⏩ SMOLVLMのさまざまな使用方法を試すには、[vlm_usage_sample.ipynb](./notebooks/vlm_usage_sample.ipynb)を試してください。
diff --git a/ja/6_synthetic_datasets/README.md b/ja/6_synthetic_datasets/README.md
new file mode 100644
index 00000000..c898cb26
--- /dev/null
+++ b/ja/6_synthetic_datasets/README.md
@@ -0,0 +1,39 @@
+# 合成データセット
+
+合成データは、実際の使用状況を模倣する人工的に生成されたデータです。データセットを拡張または強化することで、データの制限を克服することができます。合成データはすでにいくつかのユースケースで使用されていましたが、大規模な言語モデルは、言語モデルの事前および事後トレーニング、および評価のための合成データセットをより一般的にしました。
+
+私たちは、検証済みの研究論文に基づいた迅速で信頼性が高くスケーラブルなパイプラインを必要とするエンジニアのための合成データとAIフィードバックのフレームワークである[`distilabel`](https://distilabel.argilla.io/latest/)を使用します。パッケージとベストプラクティスの詳細については、[ドキュメント](https://distilabel.argilla.io/latest/)を参照してください。
+
+## モジュール概要
+
+言語モデルの合成データは、インストラクション、嗜好、批評の3つの分類に分類できます。私たちは、インストラクションチューニングと嗜好調整のためのデータセットの生成に焦点を当てます。両方のカテゴリでは、既存のデータをモデルの批評とリライトで改善するための第3のカテゴリの側面もカバーします。
+
+![合成データの分類](./images/taxonomy-synthetic-data.png)
+
+## コンテンツ
+
+### 1. [インストラクションデータセット](./instruction_datasets.md)
+
+インストラクションチューニングのためのインストラクションデータセットの生成方法を学びます。基本的なプロンプトを使用してインストラクションチューニングデータセットを作成する方法や、論文から得られたより洗練された技術を使用する方法を探ります。SelfInstructやMagpieのような方法を使用して、インコンテキスト学習のためのシードデータを使用してインストラクションチューニングデータセットを作成できます。さらに、EvolInstructを通じたインストラクションの進化についても探ります。[学び始める](./instruction_datasets.md)。
+
+### 2. [嗜好データセット](./preference_datasets.md)
+
+嗜好調整のための嗜好データセットの生成方法を学びます。セクション1で紹介した方法と技術を基に構築し、追加の応答を生成します。次に、EvolQualityプロンプトを使用して応答を改善する方法を学びます。最後に、スコアと批評を生成するUltraFeedbackプロンプトを探り、嗜好ペアを作成します。[学び始める](./preference_datasets.md)。
+
+### 演習ノートブック
+
+| タイトル | 説明 | 演習 | リンク | Colab |
+|-------|-------------|----------|------|-------|
+| インストラクションデータセット | インストラクションチューニングのためのデータセットを生成する | 🐢 インストラクションチューニングデータセットを生成する <br> 🐕 シードデータを使用してインストラクションチューニングのためのデータセットを生成する <br> 🦁 シードデータとインストラクションの進化を使用してインストラクションチューニングのためのデータセットを生成する | [リンク](./notebooks/instruction_sft_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
+| 嗜好データセット | 嗜好調整のためのデータセットを生成する | 🐢 嗜好調整データセットを生成する <br> 🐕 応答の進化を使用して嗜好調整のためのデータセットを生成する <br> 🦁 応答の進化と批評を使用して嗜好調整のためのデータセットを生成する | [リンク](./notebooks/preference_alignment_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
+
+## リソース
+
+- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Synthetic Data Generator is UI app](https://huggingface.co/blog/synthetic-data-generator)
+- [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
+- [Self-instruct](https://arxiv.org/abs/2212.10560)
+- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
+- [Magpie](https://arxiv.org/abs/2406.08464)
+- [UltraFeedback](https://arxiv.org/abs/2310.01377)
+- [Deita](https://arxiv.org/abs/2312.15685)
diff --git a/ja/6_synthetic_datasets/instruction_datasets.md b/ja/6_synthetic_datasets/instruction_datasets.md
new file mode 100644
index 00000000..b4ae48d1
--- /dev/null
+++ b/ja/6_synthetic_datasets/instruction_datasets.md
@@ -0,0 +1,197 @@
+# インストラクションデータセットの生成
+
+[インストラクションチューニングの章](../1_instruction_tuning/README.md)では、教師あり微調整によるモデルの微調整について学びました。このセクションでは、SFTのためのインストラクションデータセットの生成方法を探ります。基本的なプロンプトを使用してインストラクションチューニングデータセットを作成する方法や、論文から得られたより洗練された技術を使用する方法を探ります。インストラクションチューニングデータセットは、SelfInstructやMagpieのような方法を使用して、インコンテキスト学習のためのシードデータを使用して作成できます。さらに、EvolInstructを通じたインストラクションの進化についても探ります。最後に、distilabelパイプラインを使用してインストラクションチューニングのためのデータセットを生成する方法を探ります。
+
+## プロンプトからデータへ
+
+合成データは一見複雑に見えますが、モデルから知識を抽出するための効果的なプロンプトを作成することに簡略化できます。つまり、特定のタスクのためのデータを生成する方法と考えることができます。課題は、データが多様で代表的であることを保証しながら、効果的にプロンプトを作成することです。幸いなことに、多くの論文がこの問題を探求しており、このコースではいくつかの有用なものを探ります。まずは、手動でプロンプトを作成して合成データを生成する方法を探ります。
+
+### 基本的なプロンプト
+
+基本的な例から始めましょう。[HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)モデルを`distilabel`ライブラリの`transformers`統合を使用してロードします。`TextGeneration`クラスを使用して合成`プロンプト`を生成し、それを使用して`completion`を生成します。
+
+次に、`distilabel`ライブラリを使用してモデルをロードします。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import TextGeneration
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+gen = TextGeneration(llm=llm)
+gen.load()
+```
+
+!!! note
+    Distilabelは`llm`をメモリにロードするため、ノートブックで作業する場合は、メモリの問題を避けるために使用後に`gen.unload()`する必要があります。
+
+次に、`llm`を使用してインストラクションチューニングのための`プロンプト`を生成します。
+
+```python
+next(gen.process([{"instruction": "Hugging Faceの小規模AIモデルに関するSmol-Courseについての質問を生成してください。"}]))
+# Smol-Courseの目的は何ですか？
+```
+
+最後に、同じ`プロンプト`を入力として使用して`completion`を生成します。
+
+```python
+next(gen.process([{"instruction": "Smol-Courseの目的は何ですか？"}]))
+# Smol-Courseはコンピュータサイエンスの概念を学ぶためのプラットフォームです。
+```
+
+素晴らしい！合成`プロンプト`と対応する`completion`を生成できました。このシンプルなアプローチをスケールアップして、より多くのデータを生成することができますが、データの品質はそれほど高くなく、コースやドメインのニュアンスを考慮していません。さらに、現在のコードを再実行すると、データがそれほど多様でないことがわかります。幸いなことに、この問題を解決する方法があります。
+
+### SelfInstruct
+
+SelfInstructは、シードデータセットに基づいて新しいインストラクションを生成するプロンプトです。このシードデータは単一のインストラクションやコンテキストの一部である場合があります。プロセスは、初期のシードデータのプールから始まります。言語モデルは、インコンテキスト学習を使用してこのシードデータに基づいて新しいインストラクションを生成するようにプロンプトされます。このプロンプトは[distilabelで実装されています](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2)が、簡略化されたバージョンは以下の通りです：
+
+```
+# タスクの説明
+与えられたAIアプリケーションが受け取ることができる{{ num_instructions }}のユーザークエリを開発してください。モデルのテキスト能力内で動詞と言語構造の多様性を強調してください。
+
+# コンテキスト
+{{ input }}
+
+# 出力
+```
+
+これを使用するには、`llm`を[SelfInstructクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/)に渡す必要があります。[プロンプトからデータセクション](#prompt-to-data)のテキストをコンテキストとして使用し、新しいインストラクションを生成してみましょう。
+
+```python
+from distilabel.steps.tasks import SelfInstruct
+
+self_instruct = SelfInstruct(llm=llm)
+self_instruct.load()
+
+context = "<prompt_to_data_section>"
+
+next(self_instruct.process([{"input": text}]))["instructions"][0]
+# 手動プロンプトを使用して合成データを生成するプロセスは何ですか？
+```
+
+生成されたインストラクションはすでにかなり良くなっており、実際のコンテンツやドメインに適しています。しかし、プロンプトを進化させることでさらに良くすることができます。
+
+### EvolInstruct
+
+EvolInstructは、入力インストラクションを進化させて、同じインストラクションのより良いバージョンにするプロンプト技術です。このより良いバージョンは、一連の基準に従って定義され、元のインストラクションに制約、深化、具体化、推論、または複雑化を追加します。このプロセスは、元のインストラクションのさまざまな進化を作成するために複数回繰り返すことができ、理想的には元のインストラクションのより良いバージョンに導きます。このプロンプトは[distilabelで実装されています](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct)が、簡略化されたバージョンは以下の通りです：
+
+```
+プロンプトリライターとして行動してください。
+与えられたプロンプトを、より複雑なバージョンに書き換えてください。
+以下の基準に基づいてプロンプトを複雑化してください：
+{{ criteria }}
+
+# プロンプト
+{{ input }}
+
+# 出力
+```
+
+これを使用するには、`llm`を[EvolInstructクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/)に渡す必要があります。[SelfInstructセクション](#selfinstruct)の合成プロンプトを入力として使用し、それをより良いバージョンに進化させてみましょう。この例では、1世代だけ進化させます。
+
+```python
+from distilabel.steps.tasks import EvolInstruct
+
+evol_instruct = EvolInstruct(llm=llm, num_evolutions=1)
+evol_instruct.load()
+
+text = "手動プロンプトを使用して合成データを生成するプロセスは何ですか？"
+
+next(evol_instruct.process([{"instruction": text}]))
+# 手動プロンプトを使用して合成データを生成するプロセスは何ですか？
+# そして、人工知能システムであるGPT4が機械学習アルゴリズムを使用して入力データを合成データに変換する方法は？
+```
+
+インストラクションはより複雑になりましたが、元の意味を失っています。したがって、進化させることは両刃の剣であり、生成するデータの品質に注意する必要があります。
+
+### Magpie
+
+Magpieは、言語モデルの自己回帰的要因と、インストラクションチューニングプロセス中に使用されていた[チャットテンプレート](../1_instruction_tuning/chat_templates.md)に依存する技術です。覚えているかもしれませんが、チャットテンプレートは、システム、ユーザー、アシスタントの役割を明確に示す形式で会話を構造化します。インストラクションチューニングフェーズ中に、言語モデルはこの形式を再現するように最適化されており、Magpieはそれを利用します。チャットテンプレートに基づいた事前クエリプロンプトから始めますが、ユーザーメッセージインジケーター、例：<|im_end|>ユーザー\nの前で停止し、その後、言語モデルを使用してユーザープロンプトを生成し、アシスタントインジケーター、例：<|im_end|>まで生成します。このアプローチにより、多くのデータを非常に効率的に生成でき、マルチターンの会話にもスケールアップできます。この生成されたデータは、使用されたモデルのインストラクションチューニングフェーズのトレーニングデータを再現すると仮定されています。
+
+このシナリオでは、プロンプトテンプレートはチャットテンプレート形式に基づいているため、モデルごとに異なります。しかし、プロセスをステップバイステップで簡略化して説明できます。
+
+```bash
+# ステップ1：事前クエリプロンプトを提供する
+<|im_end|>ユーザー\n
+
+# ステップ2：言語モデルがユーザープロンプトを生成する
+<|im_end|>ユーザー\n
+Smol-Courseの目的は何ですか？
+
+# ステップ3：生成を停止する
+<|im_end|>
+```
+
+distilabelで使用するには、`llm`を[Magpieクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/)に渡す必要があります。
+
+```python
+from distilabel.steps.tasks import Magpie
+
+magpie = Magpie(llm=llm)
+magpie.load()
+
+next(magpie.process([{"system_prompt": "あなたは役立つアシスタントです。"}]))
+# [{
+#   "role": "user",
+#   "content": "トップ3の大学のリストを提供できますか？"
+# },
+# {
+#   "role": "assistant",
+#   "content": "トップ3の大学は：MIT、イェール、スタンフォードです。"
+# }]
+```
+
+すぐに`プロンプト`と`completion`を含むデータセットが得られます。ドメインに特化したパフォーマンスを向上させるために、`system_prompt`に追加のコンテキストを挿入できます。LLMが特定のドメインデータを生成するために、システムプロンプトでユーザーのクエリがどのようなものかを説明することが役立ちます。これは、ユーザープロンプトを生成する前の事前クエリプロンプトで使用され、LLMがそのドメインのユーザークエリを生成するようにバイアスをかけます。
+
+```
+あなたは数学の問題を解決するためにユーザーを支援するAIアシスタントです。
+```
+
+システムプロンプトに追加のコンテキストを渡すことは、一般的に言語モデルが最適化されていないため、カスタマイズには他の技術ほど効果的ではないことが多いです。
+
+### プロンプトからパイプラインへ
+
+これまで見てきたクラスはすべて、パイプラインで使用できるスタンドアロンのクラスです。これは良いスタートですが、`Pipeline`クラスを使用してデータセットを生成することでさらに良くすることができます。`TextGeneration`ステップを使用してインストラクションチューニングのための合成データセットを生成します。パイプラインは、データをロードするための`LoadDataFromDicts`ステップ、`プロンプト`を生成するための`TextGeneration`ステップ、およびそのプロンプトの`completion`を生成するためのステップで構成されます。ステップを接続し、`>>`演算子を使用してデータをパイプラインに流します。distilabelの[ドキュメント](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/#input-output-columns)には、ステップの入力および出力列に関する情報が記載されています。データがパイプラインを正しく流れるようにするために、`output_mappings`パラメータを使用して出力列を次のステップの入力列にマッピングします。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps import LoadDataFromDicts
+from distilabel.steps.tasks import TextGeneration
+
+with Pipeline() as pipeline:
+    data = LoadDataFromDicts(data=[{"instruction": "Hugging Faceの小規模AIモデルに関するSmol-Courseについての短い質問を生成してください。"}])
+    llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+    gen_a = TextGeneration(llm=llm, output_mappings={"generation": "instruction"})
+    gen_b = TextGeneration(llm=llm, output_mappings={"generation": "response"})
+    data >> gen_a >> gen_b
+
+if __name__ == "__main__":
+    distiset = pipeline.run(use_cache=False)
+    print(distiset["default"]["train"][0])
+# [{
+#   "instruction": "Smol-Courseの目的は何ですか？",
+#   "response": "Smol-Courseはコンピュータサイエンスの概念を学ぶためのプラットフォームです。"
+# }]
+```
+
+このパイプラインの背後には多くのクールな機能があります。生成結果を自動的にキャッシュするため、生成ステップを再実行する必要がありません。フォールトトレランスが組み込まれており、生成ステップが失敗してもパイプラインは実行を続けます。また、すべての生成ステップを並行して実行するため、生成が高速です。`draw`メソッドを使用してパイプラインを視覚化することもできます。ここでは、データがパイプラインをどのように流れ、`output_mappings`が出力列を次のステップの入力列にどのようにマッピングするかを確認できます。
+
+![Pipeline](./images/pipeline.png)
+
+## ベストプラクティス
+
+- 多様なシードデータを確保して、さまざまなシナリオをカバーする
+- 生成されたデータが多様で高品質であることを定期的に評価する
+- データの品質を向上させるために（システム）プロンプトを繰り返し改善する
+
+## 次のステップ
+
+👨🏽‍💻 コード - [演習ノートブック](./notebooks/instruction_sft_dataset.ipynb)でインストラクションチューニングのためのデータセットを生成する
+🧑‍🏫 学ぶ - [preference datasetsの生成](./preference_datasets.md)について学ぶ
+
+## 参考文献
+
+- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Self-instruct](https://arxiv.org/abs/2212.10560)
+- [Evol-Instruct](https://arxiv.org/abs/2304.12244)
+- [Magpie](https://arxiv.org/abs/2406.08464)
diff --git a/ja/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/ja/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
new file mode 100644
index 00000000..69c72fbb
--- /dev/null
+++ b/ja/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# インストラクションチューニングのためのデータセットを生成する\n",
+    "\n",
+    "このノートブックでは、インストラクションチューニングのためのデータセットを生成するプロセスを案内します。`distilabel`パッケージを使用してインストラクションチューニングのためのデータセットを生成します。\n",
+    "\n",
+    "それでは、インストラクションチューニングデータセットを見てみましょう。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>演習: インストラクションチューニングのためのデータセットを生成する</h2>\n",
+    "    <p>インストラクションチューニングのためのデータセットを生成する方法を見たので、実際にインストラクションチューニングのためのデータセットを生成してみましょう。</p>\n",
+    "    <p><b>難易度レベル</b></p>\n",
+    "    <p>🐢 インストラクションチューニングデータセットを生成する</p>\n",
+    "    <p>🐕 シードデータを使用してインストラクションチューニングのためのデータセットを生成する</p>\n",
+    "    <p>🦁 シードデータとインストラクションの進化を使用してインストラクションチューニングのためのデータセットを生成する</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## 依存関係のインストール\n",
+    "\n",
+    "transformersの代わりに、`vllm`や`hf-inference-endpoints`をインストールすることもできます。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 合成の開始\n",
+    "\n",
+    "前のコース内容で見たように、インストラクションデータセット生成のためのdistilabelパイプラインを作成できます。最低限のパイプラインはすでに提供されています。このパイプラインをスケールアップして、インストラクションチューニングのための大規模なデータセットを生成してください。モデル、モデルプロバイダー、生成引数を入れ替えて、データセットの品質にどのように影響するかを確認してください。小規模に実験し、後でスケールアップしましょう。\n",
+    "\n",
+    "[distilabelコンポーネントギャラリー](https://distilabel.argilla.io/latest/components-gallery/)で、処理クラスの情報とその使用方法を確認してください。\n",
+    "\n",
+    "辞書からデータをロードする代わりに、Hubからデータをロードする例を以下に示します。\n",
+    "\n",
+    "```python\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "with Pipeline(...) as pipeline:\n",
+    "    ...\n",
+    "\n",
+    "if __name__ == \"__main__:\n",
+    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
+    "    distiset = pipeline.run(dataset=dataset)\n",
+    "```\n",
+    "\n",
+    "パイプラインを実行した後、データセットをHubにプッシュするのを忘れないでください！"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distilabel.llms import TransformersLLM\n",
+    "from distilabel.pipeline import Pipeline\n",
+    "from distilabel.steps import LoadDataFromDicts\n",
+    "from distilabel.steps.tasks import TextGeneration\n",
+    "\n",
+    "with Pipeline() as pipeline:\n",
+    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Hugging Faceの小規模AIモデルに関するSmol-Courseについての短い質問を生成してください。\"}])\n",
+    "    llm = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
+    "    gen_a = TextGeneration(llm=llm, output_mappings={\"generation\": \"instruction\"})\n",
+    "    gen_b = TextGeneration(llm=llm, output_mappings={\"generation\": \"response\"})\n",
+    "    data >> gen_a >> gen_b\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    distiset = pipeline.run(use_cache=False)\n",
+    "    distiset.push_to_hub(\"huggingface-smol-course-instruction-tuning-dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌯 まとめ\n",
+    "\n",
+    "インストラクションチューニングのためのデータセットを生成する方法を見てきました。これを使用して次のことができます：\n",
+    "\n",
+    "- インストラクションチューニングのためのデータセットを生成する。\n",
+    "- インストラクションチューニングのための評価データセットを作成する。\n",
+    "\n",
+    "次に\n",
+    "\n",
+    "🧑‍🏫 学ぶ - [preference datasetsの生成](./preference_datasets.md)について学ぶ\n",
+    "🏋️‍♂️ 合成データセットに基づいて[インストラクションチューニングの章](../../1_instruction_tuning/README.md)でモデルを微調整する\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ja/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/ja/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
new file mode 100644
index 00000000..2dc1835b
--- /dev/null
+++ b/ja/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 嗜好調整のためのデータセットを生成する\n",
+    "\n",
+    "このノートブックでは、嗜好調整のためのデータセットを生成するプロセスを案内します。`distilabel`パッケージを使用して嗜好調整のためのデータセットを生成します。\n",
+    "\n",
+    "それでは、嗜好調整データセットを見てみましょう。\n",
+    "\n",
+    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "    <h2 style='margin: 0;color:blue'>演習: 嗜好調整のためのデータセットを生成する</h2>\n",
+    "    <p>嗜好調整のためのデータセットを生成する方法を見たので、実際に嗜好調整のためのデータセットを生成してみましょう。</p>\n",
+    "    <p><b>難易度レベル</b></p>\n",
+    "    <p>🐢 嗜好調整のためのデータセットを生成する</p>\n",
+    "    <p>🐕 応答の進化を使用して嗜好調整のためのデータセットを生成する</p>\n",
+    "    <p>🦁 応答の進化とモデルプーリングを使用して嗜好調整のためのデータセットを生成する</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## 依存関係のインストール\n",
+    "\n",
+    "transformersの代わりに、`vllm`や`hf-inference-endpoints`をインストールすることもできます。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"distilabel[hf-transformers,outlines,instructor]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 合成の開始\n",
+    "\n",
+    "前のノートブックで見たように、嗜好データセット生成のためのdistilabelパイプラインを作成できます。最低限のパイプラインはすでに提供されています。このパイプラインをスケールアップして、嗜好調整のための大規模なデータセットを生成してください。モデル、モデルプロバイダー、生成引数を入れ替えて、データセットの品質にどのように影響するかを確認してください。小規模に実験し、後でスケールアップしましょう。\n",
+    "\n",
+    "[distilabelコンポーネントギャラリー](https://distilabel.argilla.io/latest/components-gallery/)で、処理クラスの情報とその使用方法を確認してください。\n",
+    "\n",
+    "辞書からデータをロードする代わりに、Hubからデータをロードする例を以下に示します。\n",
+    "\n",
+    "```python\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "with Pipeline(...) as pipeline:\n",
+    "    ...\n",
+    "\n",
+    "if __name__ == \"__main__:\n",
+    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
+    "    distiset = pipeline.run(dataset=dataset)\n",
+    "```\n",
+    "\n",
+    "パイプラインを実行した後、データセットをHubにプッシュするのを忘れないでください！"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distilabel.llms import TransformersLLM\n",
+    "from distilabel.pipeline import Pipeline\n",
+    "from distilabel.steps import GroupColumns, LoadDataFromDicts\n",
+    "from distilabel.steps.tasks import TextGeneration\n",
+    "\n",
+    "with Pipeline() as pipeline:\n",
+    "    data = LoadDataFromDicts(data=[{\"instruction\": \"合成データとは何ですか？\"}])\n",
+    "    llm_a = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
+    "    gen_a = TextGeneration(llm=llm_a)\n",
+    "    llm_b = TransformersLLM(model=\"Qwen/Qwen2.5-1.5B-Instruct\")\n",
+    "    gen_b = TextGeneration(llm=llm_b)\n",
+    "    group = GroupColumns(columns=[\"generation\"])\n",
+    "    data >> [gen_a, gen_b] >> group\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    distiset = pipeline.run()\n",
+    "    distiset.push_to_hub(\"huggingface-smol-course-preference-tuning-dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌯 まとめ\n",
+    "\n",
+    "嗜好調整のためのデータセットを生成する方法を見てきました。これを使用して次のことができます：\n",
+    "\n",
+    "- 嗜好調整のためのデータセットを生成する。\n",
+    "- 嗜好調整のための評価データセットを作成する。\n",
+    "\n",
+    "次に\n",
+    "\n",
+    "🏋️‍♂️ 合成データセットに基づいて[嗜好調整の章](../../2_preference_alignment/README.md)でモデルを微調整する\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ja/6_synthetic_datasets/preference_datasets.md b/ja/6_synthetic_datasets/preference_datasets.md
new file mode 100644
index 00000000..92a29b29
--- /dev/null
+++ b/ja/6_synthetic_datasets/preference_datasets.md
@@ -0,0 +1,155 @@
+# 嗜好データセットの生成
+
+[嗜好調整の章](../2_preference_alignment/README.md)では、直接嗜好最適化について学びました。このセクションでは、DPOのような方法のための嗜好データセットを生成する方法を探ります。[インストラクションデータセットの生成](./instruction_datasets.md)で紹介した方法を基に構築します。さらに、基本的なプロンプトを使用してデータセットに追加の完了を追加する方法や、EvolQualityを使用して応答の品質を向上させる方法を示します。最後に、UltraFeedbackを使用してスコアと批評を生成する方法を示します。
+
+## 複数の完了を作成する
+
+嗜好データは、同じ`インストラクション`に対して複数の`完了`を持つデータセットです。モデルにプロンプトを与えて追加の`完了`を生成することで、データセットにより多くの`完了`を追加できます。この際、2つ目の完了が全体的な品質や表現において最初の完了とあまり似ていないことを確認する必要があります。これは、モデルが明確な嗜好に最適化される必要があるためです。通常、`選ばれた`と`拒否された`と呼ばれる完了のどちらが好まれるかを知りたいのです。[スコアの作成セクション](#creating-scores)で、選ばれた完了と拒否された完了を決定する方法について詳しく説明します。
+
+### モデルプーリング
+
+異なるモデルファミリーからモデルを使用して2つ目の完了を生成することができます。これをモデルプーリングと呼びます。2つ目の完了の品質をさらに向上させるために、`温度`を調整するなど、異なる生成引数を使用することができます。最後に、異なるプロンプトテンプレートやシステムプロンプトを使用して2つ目の完了を生成し、特定の特性に基づいて多様性を確保することができます。理論的には、異なる品質の2つのモデルを使用し、より良いものを`選ばれた`完了として使用することができます。
+
+まず、[Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)と[HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)モデルを`transformers`統合の`distilabel`ライブラリを使用してロードします。これらのモデルを使用して、与えられた`プロンプト`に対して2つの合成`応答`を作成します。`LoadDataFromDicts`、`TextGeneration`、および`GroupColumns`を使用して別のパイプラインを作成します。最初にデータをロードし、次に2つの生成ステップを使用し、最後に結果をグループ化します。ステップを接続し、`>>`演算子と`[]`を使用してデータをパイプラインに流します。これは、前のステップの出力を次のステップの入力として使用することを意味します。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.pipeline import Pipeline
+from distilabel.steps import GroupColumns, LoadDataFromDicts
+from distilabel.steps.tasks import TextGeneration
+
+with Pipeline() as pipeline:
+    data = LoadDataFromDicts(data=[{"instruction": "合成データとは何ですか？"}])
+    llm_a = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+    gen_a = TextGeneration(llm=llm_a)
+    llm_b = TransformersLLM(model="Qwen/Qwen2.5-1.5B-Instruct")
+    gen_b = TextGeneration(llm=llm_b)
+    group = GroupColumns(columns=["generation"])
+    data >> [gen_a, gen_b] >> group
+
+if __name__ == "__main__":
+    distiset = pipeline.run()
+    print(distiset["default"]["train"]["grouped_generation"][0])
+# {[
+#   '合成データは、実際の使用状況を模倣する人工的に生成されたデータです。',
+#   '合成データとは、人工的に生成されたデータを指します。'
+# ]}
+```
+
+ご覧のとおり、与えられた`プロンプト`に対して2つの合成`完了`があります。生成ステップを特定の`システムプロンプト`で初期化するか、`TransformersLLM`に生成引数を渡すことで、多様性をさらに向上させることができます。次に、EvolQualityを使用して`完了`の品質を向上させる方法を見てみましょう。
+
+### EvolQuality
+
+EvolQualityは、[EvolInstruct](./instruction_datasets.md#evolinstruct)に似ていますが、入力`プロンプト`の代わりに`完了`を進化させます。このタスクは、`プロンプト`と`完了`の両方を受け取り、一連の基準に基づいて`完了`をより良いバージョンに進化させます。このより良いバージョンは、役立ち度、関連性、深化、創造性、または詳細の基準に基づいて定義されます。これにより、データセットに追加の`完了`を自動的に生成できます。理論的には、進化したバージョンが元の完了よりも優れていると仮定し、それを`選ばれた`完了として使用することができます。
+
+プロンプトは[distilabelで実装されています](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality)が、簡略化されたバージョンは以下の通りです：
+
+```bash
+応答リライターとして行動してください。
+与えられたプロンプトと応答を、より良いバージョンに書き換えてください。
+以下の基準に基づいてプロンプトを複雑化してください：
+{{ criteria }}
+
+# プロンプト
+{{ input }}
+
+# 応答
+{{ output }}
+
+# 改善された応答
+```
+
+これを使用するには、[EvolQualityクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/)に`llm`を渡す必要があります。[モデルプーリングセクション](#model-pooling)の合成`プロンプト`と`完了`を使用して、より良いバージョンに進化させてみましょう。この例では、1世代だけ進化させます。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import EvolQuality
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+evol_quality = EvolQuality(llm=llm, num_evolutions=1)
+evol_quality.load()
+
+instruction = "合成データとは何ですか？"
+completion = "合成データは、実際の使用状況を模倣する人工的に生成されたデータです。"
+
+next(evol_quality.process([{
+    "instruction": instruction,
+    "response": completion
+}]))
+# 手動プロンプトを使用して合成データを生成するプロセスは何ですか？
+```
+
+`応答`はより複雑で、`インストラクション`に特化しています。これは良いスタートですが、EvolInstructで見たように、進化した生成物が常に優れているわけではありません。したがって、データセットの品質を確保するために追加の評価技術を使用することが重要です。次のセクションでこれを探ります。
+
+## スコアの作成
+
+スコアは、ある応答が他の応答よりもどれだけ好まれるかを測定するものです。一般的に、これらのスコアは絶対的、主観的、または相対的です。このコースでは、最初の2つに焦点を当てます。これらは嗜好データセットの作成に最も価値があるためです。このスコアリングは、評価技術と重なる部分があり、[評価の章](../3_evaluation/README.md)で見たように、通常はより大きなモデルが必要です。
+
+### UltraFeedback
+
+UltraFeedbackは、与えられた`プロンプト`とその`完了`に対してスコアと批評を生成する技術です。
+
+スコアは、一連の基準に基づいて`完了`の品質を評価します。基準には、`役立ち度`、`関連性`、`深化`、`創造性`の4つの細かい基準があります。これらは便利ですが、一般的には全体的な基準を使用するのが良いスタートです。これにより、スコアの生成プロセスが簡略化されます。スコアは、どの`完了`が`選ばれた`もので、どれが`拒否された`ものであるかを決定するために使用できます。絶対的なスコアであるため、データセットの外れ値を見つけるための興味深いフィルターとしても使用できます。たとえば、最悪の完了や差が大きいペアを見つけることができます。
+
+批評はスコアの理由を提供するために追加されます。これにより、スコアの違いを理解するための追加のコンテキストが提供されます。言語モデルは広範な批評を生成しますが、これは非常に便利ですが、スコアを表す単一のトークンを生成するよりもコストと複雑さが増します。
+
+プロンプトは[distilabelで実装されています](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback)が、簡略化されたバージョンは以下の通りです：
+
+```bash
+モデルの出力をさまざまな基準に基づいて評価してください：役立ち度、関連性、深化、創造性
+上記の要因に基づいて全体的な評価を提供する役割を果たします。
+出力の全体的な品質を1から5のスコアで評価してください。
+
+以下の形式で回答してください：スコア - 理由
+
+# 入力
+{{ input }}
+
+# 応答
+{{ output }}
+
+# スコア - 理由
+```
+
+これを使用するには、[UltraFeedbackクラス](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/)に`llm`を渡す必要があります。[モデルプーリングセクション](#model-pooling)の合成`プロンプト`と`完了`を評価してみましょう。
+
+```python
+from distilabel.llms import TransformersLLM
+from distilabel.steps.tasks import UltraFeedback
+
+llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
+ultrafeedback = UltraFeedback(llm=llm)
+ultrafeedback.load()
+
+instruction = "合成データとは何ですか？"
+completion_a = "合成データは、実際の使用状況を模倣する人工的に生成されたデータです。"
+completion_b = "合成データとは、人工的に生成されたデータを指します。"
+
+next(ultrafeedback.process([{
+    "instruction": instruction,
+    "generations": [completion_a, completion_b]
+}]))
+# [
+#     {
+#         'ratings': [4, 5],
+#         'rationales': ['could have been more specific', 'good definition'],
+#     }
+# ]
+```
+
+## ベストプラクティス
+
+- 全体的なスコアは、批評や特定のスコアよりも安価で生成が容易です
+- スコアや批評を生成するために大きなモデルを使用する
+- スコアや批評を生成するために多様なモデルセットを使用する
+- `system_prompt`やモデルの構成を繰り返し改善する
+
+## 次のステップ
+
+👨🏽‍💻 コード -[演習ノートブック](./notebooks/preference_dpo_dataset.ipynb)でインストラクションチューニングのためのデータセットを生成する
+
+## 参考文献
+
+- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Deita](https://arxiv.org/abs/2312.15685)
+- [UltraFeedback](https://arxiv.org/abs/2310.01377)

From 6b7453676829ed82115a5ab600b57f9e6310072b Mon Sep 17 00:00:00 2001
From: Ikko Ashimine <ashimine_ikko_bp@tenso.com>
Date: Mon, 20 Jan 2025 15:54:06 +0900
Subject: [PATCH 52/63] docs: add image files to ja directory

---
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 ja/6_synthetic_datasets/images/pipeline.png   | Bin 0 -> 24977 bytes
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 create mode 100644 ja/5_vision_language_models/images/VLM_Architecture.png
 create mode 100644 ja/5_vision_language_models/images/VLM_Process.png
 create mode 100644 ja/5_vision_language_models/images/VLM_Usage.png
 create mode 100644 ja/6_synthetic_datasets/images/pipeline.png
 create mode 100644 ja/6_synthetic_datasets/images/taxonomy-synthetic-data.png

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HcmV?d00001


From 3c9a5832560fdc7968941bcd6c32a845d071692a Mon Sep 17 00:00:00 2001
From: burtenshaw <ben.burtenshaw@gmail.com>
Date: Mon, 20 Jan 2025 19:55:06 +0100
Subject: [PATCH 53/63] Update README.md

---
 7_inference/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/7_inference/README.md b/7_inference/README.md
index f609f57f..41df86d5 100644
--- a/7_inference/README.md
+++ b/7_inference/README.md
@@ -16,7 +16,7 @@ Learn how to use the Hugging Face Transformers pipeline for basic inference. We'
 
 ### 2. [Production Inference with TGI](./tgi_inference.md)
 
-Learn how to deploy models for production using Text Generation Inference. We'll explore optimized serving techniques, batching strategies, and monitoring solutions. TGI provides production-ready features like health checks, metrics, and Docker deployment options. [Start learning](./tgi_inference.md).
+Learn how to deploy models for production using Text Generation Inference. We'll explore optimized serving techniques, batching strategies, and monitoring solutions. TGI provides production-ready features like health checks, metrics, and Docker deployment options. [Start learning](./text_generation_inference.md).
 
 ### Exercise Notebooks
 
@@ -33,4 +33,4 @@ Learn how to deploy models for production using Text Generation Inference. We'll
 - [TGI GitHub Repository](https://github.com/huggingface/text-generation-inference)
 - [Hugging Face Model Deployment Documentation](https://huggingface.co/docs/inference-endpoints/index)
 - [vLLM: High-throughput LLM Serving](https://github.com/vllm-project/vllm)
-- [Optimizing Transformer Inference](https://huggingface.co/blog/optimize-transformer-inference)
\ No newline at end of file
+- [Optimizing Transformer Inference](https://huggingface.co/blog/optimize-transformer-inference)

From e64d0720c69d1673fb8f1fa172403789121c12cf Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 22 Jan 2025 20:37:05 +0100
Subject: [PATCH 54/63] init

---
 .../notebooks/vlm_sft_sample.ipynb            | 165 +++++++++---------
 1 file changed, 81 insertions(+), 84 deletions(-)

diff --git a/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
index 5af7d6a2..65fb7301 100644
--- a/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
+++ b/vi/5_vision_language_models/notebooks/vlm_sft_sample.ipynb
@@ -4,17 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Fine-Tuning a VLM \n",
+    "# Tinh chỉnh Mô hình Ngôn ngữ Thị giác (VLM)\n",
     "\n",
-    "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolVLM-Instruct` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n",
+    "Notebook này minh họa cách tinh chỉnh mô hình `HuggingFaceTB/SmolVLM-Instruct` bằng cách sử dụng `SFTTrainer` từ thư viện `trl`. Các ô của notebook sẽ chạy và tinh chỉnh mô hình. Bạn có thể chọn mức độ khó bằng cách thử các tập dữ liệu khác nhau.\n",
     "\n",
     "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Fine-Tuning SmolVLM with SFTTrainer</h2>\n",
-    "    <p>Take a dataset from the Hugging Face hub and finetune a model on it. </p> \n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Use the `HuggingFaceM4/ChartQA` dataset for SFT training run.</p>\n",
-    "    <p>🐕 Use the fine-tuned to model generate a response, and improve upon the base example.</p>\n",
-    "    <p>🦁 Try out the other datasets and show improvement.</p>\n",
+    "<h2 style='margin: 0;color:blue'>Bài tập: Tinh chỉnh SmolVLM với SFTTrainer</h2>\n",
+    "<p>Hãy chọn một tập dữ liệu từ Hugging Face Hub và tinh chỉnh một mô hình trên đó.</p>\n",
+    "<p><b>Các mức độ khó</b></p>\n",
+    "<p>🐢 Sử dụng tập dữ liệu HuggingFaceM4/ChartQA để chạy huấn luyện SFT.</p>\n",
+    "<p>🐕 Sử dụng mô hình đã được tinh chỉnh để tạo ra phản hồi và cải thiện dựa trên ví dụ cơ bản.</p>\n",
+    "<p>🦁 Thử các tập dữ liệu khác và cho thấy sự cải thiện.</p>\n",
     "</div>"
    ]
   },
@@ -24,14 +24,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install the requirements in Google Colab\n",
+    "# Cài đặt các thư viện cần thiết trong Google Colab\n",
     "# !pip install transformers datasets trl huggingface_hub\n",
     "\n",
-    "# Authenticate to Hugging Face\n",
+    "# Xác thực với Hugging Face\n",
     "from huggingface_hub import login\n",
     "login()\n",
     "\n",
-    "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
+    "# Để thuận tiện, bạn có thể tạo một biến môi trường chứa token Hugging Face của mình là HF_TOKEN"
    ]
   },
   {
@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Import dependencies\n",
+    "# Nhập các thư viện cần thiết\n",
     "\n",
     "import torch\n",
     "import os\n",
@@ -83,7 +83,7 @@
     ").to(device)\n",
     "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
     "\n",
-    "# Set our name for the finetune to be saved &/ uploaded to\n",
+    "# Đặt tên cho bản tinh chỉnh để lưu và tải lên\n",
     "finetune_name = \"SmolVLM-FT-MyDataset\"\n",
     "finetune_tags = [\"smol-course\", \"module_5\"]"
    ]
@@ -92,11 +92,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Dataset Preparation\n",
+    "## Chuẩn bị tập dữ liệu\n",
     "\n",
-    "We will load a sample dataset and format it for training. The dataset should be structured with input-output pairs, where each input is a prompt and the output is the expected response from the model.\n",
+    "Chúng ta sẽ tải một tập dữ liệu mẫu và định dạng nó để huấn luyện. Tập dữ liệu phải được cấu trúc với các cặp đầu vào-đầu ra, trong đó mỗi đầu vào là một lời nhắc (prompt) và đầu ra là phản hồi mong đợi từ mô hình.\n",
     "\n",
-    "**TRL will format input messages based on the model's chat templates.** They need to be represented as a list of dictionaries with the keys: `role` and `content`,."
+    "**TRL sẽ định dạng các thông báo đầu vào dựa trên các mẫu trò chuyện (chat templates) của mô hình.** Chúng cần được biểu diễn dưới dạng danh sách các từ điển với các khóa: `role` và `content`."
    ]
   },
   {
@@ -129,10 +129,10 @@
     }
    ],
    "source": [
-    "# Load a sample dataset\n",
+    "# Tải tập dữ liệu mẫu\n",
     "from datasets import load_dataset\n",
     "\n",
-    "# TODO: define your dataset and config using the path and name parameters\n",
+    "# TODO: xác định tập dữ liệu và cấu hình của bạn bằng cách sử dụng các tham số đường dẫn và tên\n",
     "dataset_name = \"HuggingFaceM4/ChartQA\"\n",
     "ds = load_dataset(path=dataset_name)\n",
     "ds"
@@ -169,18 +169,18 @@
     "import matplotlib.pyplot as plt\n",
     "sample = ds['train'][3]\n",
     "\n",
-    "# Display the structure of a single sample\n",
+    "# Hiển thị cấu trúc của một mẫu đơn\n",
     "sample['image_size'] = sample['image'].size,\n",
     "\n",
-    "# Visualize the image and related metadata\n",
+    "# Trực quan hóa hình ảnh và siêu dữ liệu liên quan\n",
     "plt.imshow(sample['image'])\n",
     "plt.axis(\"off\")\n",
-    "plt.title(\"Sample Chart Image\")\n",
+    "plt.title(\"Hình ảnh biểu đồ mẫu\")\n",
     "plt.show()\n",
     "\n",
     "print(sample)\n",
     "\n",
-    "# Preprocess the sample\n",
+    "# Tiền xử lý mẫu\n",
     "prompt = [{\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": sample[\"query\"]}]}]\n",
     "formatted_query = processor.apply_chat_template(prompt, tokenize=False)\n",
     "\n",
@@ -191,25 +191,25 @@
     ").to(device)\n",
     "inputs = {key: val.to(device, dtype=torch.bfloat16) if val.dtype == torch.float else val.to(device) for key, val in inputs.items()}\n",
     "\n",
-    "# Generate predictions\n",
+    "# Tạo dự đoán\n",
     "with torch.no_grad():\n",
     "    outputs = model.generate(**inputs,\n",
     "                             max_length=1600)\n",
     "\n",
-    "# Decode the prediction\n",
+    "# Giải mã dự đoán\n",
     "prediction = processor.batch_decode(outputs, skip_special_tokens=True)\n",
     "\n",
-    "# Display the result\n",
-    "print(f\"Query: {sample['query']}\")\n",
-    "print(f\"Expected Answer: {sample['label'][0]}\")\n",
-    "print(f\"Model Prediction: {prediction[0]}\")"
+    "# Hiển thị kết quả\n",
+    "print(f\"Truy vấn: {sample['query']}\")\n",
+    "print(f\"Câu trả lời mong đợi: {sample['label'][0]}\")\n",
+    "print(f\"Dự đoán của mô hình: {prediction[0]}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Set Up LoRA"
+    "## Thiết lập LoRA"
    ]
   },
   {
@@ -228,7 +228,7 @@
    "source": [
     "from peft import LoraConfig, get_peft_model\n",
     "\n",
-    "# Configure LoRA\n",
+    "# Cấu hình LoRA\n",
     "peft_config = LoraConfig(\n",
     "    lora_alpha=16,\n",
     "    lora_dropout=0.05,\n",
@@ -238,10 +238,10 @@
     "    task_type=\"CAUSAL_LM\",\n",
     ")\n",
     "\n",
-    "# Apply PEFT model adaptation\n",
+    "# Áp dụng điều chỉnh mô hình PEFT\n",
     "peft_model = get_peft_model(model, peft_config)\n",
     "\n",
-    "# Print trainable parameters\n",
+    "# In các tham số có thể huấn luyện\n",
     "peft_model.print_trainable_parameters()"
    ]
   },
@@ -249,9 +249,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Configuring the Trainer\n",
+    "## Cấu hình Trình huấn luyện (Trainer)\n",
     "\n",
-    "The `Trainer` is configured with various parameters that control the training process. These include the number of training steps, batch size, learning rate, and evaluation strategy. Adjust these parameters based on your specific requirements and computational resources."
+    "`Trainer` được định cấu hình với nhiều tham số khác nhau để kiểm soát quá trình huấn luyện. Chúng bao gồm số bước huấn luyện, kích thước batch, tốc độ học và chiến lược đánh giá. Điều chỉnh các tham số này dựa trên các yêu cầu cụ thể và tài nguyên tính toán của bạn."
    ]
   },
   {
@@ -261,19 +261,19 @@
    "outputs": [],
    "source": [
     "def collate_fn(examples):\n",
-    "    # System message template for the VLM\n",
-    "    system_message = \"\"\"You are a Vision Language Model specialized in interpreting visual data from chart images.\n",
-    "    Your task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\n",
-    "    The charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\n",
-    "    Focus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.\"\"\"\n",
+    "    # Mẫu thông báo hệ thống cho VLM\n",
+    "    system_message = \"\"\"Bạn là một Mô hình Ngôn ngữ Thị giác chuyên giải thích dữ liệu trực quan từ hình ảnh biểu đồ.\n",
+    "    Nhiệm vụ của bạn là phân tích hình ảnh biểu đồ được cung cấp và trả lời các truy vấn bằng các câu trả lời ngắn gọn, thường là một từ, số hoặc cụm từ ngắn.\n",
+    "    Các biểu đồ bao gồm nhiều loại khác nhau (ví dụ: biểu đồ đường, biểu đồ cột) và chứa màu sắc, nhãn và văn bản.\n",
+    "    Tập trung vào việc cung cấp các câu trả lời chính xác, ngắn gọn dựa trên thông tin trực quan. Tránh giải thích thêm trừ khi thực sự cần thiết.\"\"\"\n",
     "\n",
-    "    # Initialize lists for text and image inputs\n",
+    "    # Khởi tạo danh sách cho đầu vào văn bản và hình ảnh\n",
     "    text_inputs = []\n",
     "    image_inputs = []\n",
     "\n",
-    "    # Process all examples in one loop\n",
+    "    # Xử lý tất cả các ví dụ trong một vòng lặp\n",
     "    for example in examples:\n",
-    "        # Format the chat structure for the processor\n",
+    "        # Định dạng cấu trúc trò chuyện cho bộ xử lý\n",
     "        formatted_example = {\n",
     "            \"messages\": [\n",
     "                {\n",
@@ -294,16 +294,16 @@
     "                },\n",
     "            ]\n",
     "        }\n",
-    "        # Apply chat template and strip extra spaces\n",
+    "        # Áp dụng mẫu trò chuyện và loại bỏ khoảng trắng thừa\n",
     "        text_inputs.append(processor.apply_chat_template(formatted_example[\"messages\"], tokenize=False).strip())\n",
     "        \n",
-    "        # Ensure images are in RGB mode\n",
+    "        # Đảm bảo hình ảnh ở chế độ RGB\n",
     "        image = example[\"image\"]\n",
     "        if image.mode != 'RGB':\n",
     "            image = image.convert('RGB')\n",
     "        image_inputs.append( [image] )\n",
     "\n",
-    "    # Tokenize the texts and process the images\n",
+    "    # Token hóa văn bản và xử lý hình ảnh\n",
     "    batch = processor(\n",
     "        text=text_inputs,\n",
     "        images=image_inputs,\n",
@@ -311,23 +311,22 @@
     "        padding=True\n",
     "    )\n",
     "\n",
-    "    # Clone input IDs for labels\n",
+    "    # Sao chép ID đầu vào cho nhãn\n",
     "    labels = batch[\"input_ids\"].clone()\n",
-    "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Mask padding tokens in labels\n",
+    "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Che các token đệm (pad token) trong nhãn\n",
     "\n",
-    "    # Ensure image_token is converted to string if it is an AddedToken\n",
-    "    # In some processor, processor.image_token return a list for each image.\n",
-    "    # TODO: AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\") only have one ?\n",
+    "    # Đảm bảo image_token được chuyển đổi thành chuỗi nếu nó là AddedToken\n",
+    "    # Trong một số bộ xử lý, processor.image_token trả về một danh sách cho mỗi hình ảnh.\n",
+    "    # TODO: AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\") chỉ có một?\n",
     "    image_token_id = processor.tokenizer.convert_tokens_to_ids(str(processor.image_token))\n",
     "\n",
-    "    # Mask image token IDs in the labels\n",
+    "    # Che các ID token hình ảnh trong nhãn\n",
     "    labels[labels == image_token_id] = -100\n",
     "\n",
-    "    # Add labels back to the batch\n",
+    "    # Thêm nhãn trở lại batch\n",
     "    batch[\"labels\"] = labels\n",
     "\n",
-    "\n",
-    "    return batch\n"
+    "    return batch"
    ]
   },
   {
@@ -338,30 +337,30 @@
    "source": [
     "from trl import SFTConfig, SFTTrainer\n",
     "\n",
-    "# Configure the Trainer\n",
+    "# Cấu hình Trình huấn luyện\n",
     "training_args = SFTConfig(\n",
-    "    output_dir=\"sft_output\",  # Directory to save the model\n",
-    "    num_train_epochs=3,                     # number of training epochs\n",
-    "    per_device_train_batch_size=1,          # batch size per device during training\n",
-    "    gradient_accumulation_steps=16,         # number of steps before performing a backward/update pass\n",
-    "    gradient_checkpointing=True,            # use gradient checkpointing to save memory\n",
-    "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
-    "    logging_steps=5,                        # log every 10 steps\n",
-    "    save_strategy=\"epoch\",                  # save checkpoint every epoch\n",
-    "    learning_rate=2e-4,                     # learning rate, based on QLoRA paper\n",
-    "    bf16=True,                              # use bfloat16 precision\n",
-    "    tf32=True,                              # use tf32 precision\n",
-    "    max_grad_norm=0.3,                      # max gradient norm based on QLoRA paper\n",
-    "    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper\n",
-    "    lr_scheduler_type=\"constant\",           # use constant learning rate scheduler\n",
-    "    push_to_hub=True,                       # push model to hub\n",
-    "    gradient_checkpointing_kwargs = {\"use_reentrant\": False}, # use reentrant checkpointing\n",
+    "    output_dir=\"sft_output\",                # Thư mục để lưu mô hình\n",
+    "    num_train_epochs=3,                     # số epoch huấn luyện\n",
+    "    per_device_train_batch_size=1,          # kích thước batch trên mỗi thiết bị trong quá trình huấn luyện\n",
+    "    gradient_accumulation_steps=16,         # số bước trước khi thực hiện lan truyền ngược/cập nhật\n",
+    "    gradient_checkpointing=True,            # sử dụng checkpoint gradient để tiết kiệm bộ nhớ\n",
+    "    optim=\"adamw_torch_fused\",              # sử dụng trình tối ưu hóa adamw_torch_fused\n",
+    "    logging_steps=5,                        # ghi log sau mỗi 5 bước\n",
+    "    save_strategy=\"epoch\",                  # lưu checkpoint sau mỗi epoch\n",
+    "    learning_rate=2e-4,                     # tốc độ học, dựa trên bài báo QLoRA\n",
+    "    bf16=True,                              # sử dụng độ chính xác bfloat16\n",
+    "    tf32=True,                              # sử dụng độ chính xác tf32\n",
+    "    max_grad_norm=0.3,                      # chuẩn gradient tối đa dựa trên bài báo QLoRA\n",
+    "    warmup_ratio=0.03,                      # tỷ lệ khởi động dựa trên bài báo QLoRA\n",
+    "    lr_scheduler_type=\"constant\",           # sử dụng bộ lập lịch tốc độ học không đổi\n",
+    "    push_to_hub=True,                       # đẩy mô hình lên hub\n",
+    "    gradient_checkpointing_kwargs = {\"use_reentrant\": False}, # sử dụng checkpointing reentrant\n",
     "    # dataloader_num_workers=16, \n",
-    "    dataset_text_field=\"\", # need a dummy field for collator\n",
-    "    dataset_kwargs = {\"skip_prepare_dataset\": True}, # important for collator\n",
-    "    remove_unused_columns = False                    # necessary else features except label will be removed\n",
+    "    dataset_text_field=\"\",                           # cần một trường giả cho bộ đối chiếu (collator)\n",
+    "    dataset_kwargs = {\"skip_prepare_dataset\": True}, # quan trọng cho bộ đối chiếu\n",
+    "    remove_unused_columns = False                    # cần thiết nếu không các đặc trưng ngoại trừ nhãn sẽ bị xóa\n",
     ")\n",
-    "# Initialize the Trainer\n",
+    "# Khởi tạo Trình huấn luyện\n",
     "trainer = SFTTrainer(\n",
     "    model=model,\n",
     "    args=training_args,\n",
@@ -372,16 +371,16 @@
     "    tokenizer=processor.tokenizer,\n",
     ")\n",
     "\n",
-    "# TODO: 🦁 🐕 align the SFTTrainer params with your chosen dataset. For example, if you are using the `bigcode/the-stack-smol` dataset, you will need to choose the `content` column`"
+    "# TODO: 🦁 🐕 điều chỉnh các tham số SFTTrainer với tập dữ liệu bạn đã chọn. Ví dụ: nếu bạn đang sử dụng tập dữ liệu `bigcode/the-stack-smol`, bạn sẽ cần chọn cột `content`"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Training the Model\n",
+    "## Huấn luyện mô hình\n",
     "\n",
-    "With the trainer configured, we can now proceed to train the model. The training process will involve iterating over the dataset, computing the loss, and updating the model's parameters to minimize this loss."
+    "Với trình huấn luyện được cấu hình, giờ đây chúng ta có thể tiến hành huấn luyện mô hình. Quá trình huấn luyện sẽ bao gồm việc lặp qua tập dữ liệu, tính toán hàm mất mát (loss) và cập nhật các tham số của mô hình để giảm thiểu hàm mất mát này."
    ]
   },
   {
@@ -427,13 +426,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💐 You're done!\n",
-    "\n",
-    "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolVLM` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n",
+    "## 💐  Chúc mừng bạn đã hoàn thành bài tập!\n",
+    "Notebook này đã cung cấp hướng dẫn từng bước để tinh chỉnh mô hình `HuggingFaceTB/SmolVLM` bằng cách sử dụng `SFTTrainer`. Bằng cách làm theo các bước này, bạn có thể điều chỉnh mô hình để thực hiện các tác vụ cụ thể hiệu quả hơn. Nếu bạn muốn tiếp tục làm việc với khóa học này, đây là các bước bạn có thể thử:\n",
     "\n",
-    "- Try this notebook on a harder difficulty\n",
-    "- Review a colleagues PR\n",
-    "- Improve the course material via an Issue or PR."
+    "- Thử notebook này ở độ khó cao hơn\n",
+    "- Cải thiện tài liệu khóa học thông qua một Issue hoặc PR."
    ]
   }
  ],

From ea34cd43bf24e3c4cf0f3561e9dea15996e27ae3 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 22 Jan 2025 20:45:02 +0100
Subject: [PATCH 55/63] init

---
 .../notebooks/vlm_usage_sample.ipynb          | 121 +++++++++---------
 1 file changed, 63 insertions(+), 58 deletions(-)

diff --git a/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb b/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
index 59f29d42..f8bd393f 100644
--- a/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
+++ b/vi/5_vision_language_models/notebooks/vlm_usage_sample.ipynb
@@ -4,14 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Processing images and text with VLMs \n",
+    "# Xử lý hình ảnh và văn bản với Mô hình Ngôn ngữ Thị giác (VLM)\n",
     "\n",
-    "This notebook demonstrates how to utilize the `HuggingFaceTB/SmolVLM-Instruct` 4bit-quantized model for various multimodal tasks such as:\n",
-    "- Visual Question Answering (VQA): Answering questions based on image content.\n",
-    "- Text Recognition (OCR): Extracting and interpreting text in images.\n",
-    "- Video Understanding: Describing videos through sequential frame analysis.\n",
+    "Notebook này minh họa cách sử dụng mô hình `HuggingFaceTB/SmolVLM-Instruct` đã được lượng tử hóa **4 bit** cho các tác vụ đa phương thức (multimodal) khác nhau như:\n",
     "\n",
-    "By structuring prompts effectively, you can leverage the model for many applications, such as scene understanding, document analysis, and dynamic visual reasoning."
+    "- Hỏi đáp bằng hình ảnh (Visual Question Answering - VQA): Trả lời các câu hỏi dựa trên nội dung hình ảnh.\n",
+    "\n",
+    "- Nhận dạng văn bản (OCR): Trích xuất và diễn giải văn bản trong hình ảnh.\n",
+    "\n",
+    "- Mô tả video: Mô tả video thông qua phân tích khung hình tuần tự.\n",
+    "\n",
+    "Bằng cách cấu trúc các lời nhắc (prompt) một cách hiệu quả, bạn có thể tận dụng mô hình cho nhiều ứng dụng, chẳng hạn như hiểu cảnh, phân tích tài liệu và suy luận hình ảnh động."
    ]
   },
   {
@@ -20,12 +23,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install the requirements in Google Colab\n",
+    "# Cài đặt các yêu cầu trong Google Colab\n",
     "# !pip install transformers datasets trl huggingface_hub bitsandbytes\n",
     "\n",
-    "# Authenticate to Hugging Face\n",
+    "# Xác thực với Hugging Face\n",
     "from huggingface_hub import notebook_login\n",
-    "notebook_login()\n"
+    "notebook_login()"
    ]
   },
   {
@@ -76,15 +79,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Processing Images"
+    "## Xử lý hình ảnh\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's start with generating captions and answering questions about an image. We'll also explore processing multiple images.\n",
-    "### 1. Single Image Captioning"
+    "Hãy bắt đầu với việc tạo chú thích và trả lời các câu hỏi về một hình ảnh. Chúng ta cũng sẽ khám phá việc xử lý nhiều hình ảnh.\n",
+    "\n",
+    "### 1. Tạo chú thích cho một hình ảnh"
    ]
   },
   {
@@ -149,41 +153,41 @@
     }
    ],
    "source": [
-    "# Load  one image\n",
+    "# Tải một hình ảnh\n",
     "image1 = load_image(image_url1)\n",
     "\n",
-    "# Create input messages\n",
+    "# Tạo các thông báo đầu vào\n",
     "messages = [\n",
     "    {\n",
     "        \"role\": \"user\",\n",
     "        \"content\": [\n",
     "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"Can you describe the image?\"}\n",
+    "            {\"type\": \"text\", \"text\": \"Bạn có thể mô tả hình ảnh này không?\"}\n",
     "        ]\n",
     "    },\n",
     "]\n",
     "\n",
-    "# Prepare inputs\n",
+    "# Chuẩn bị đầu vào\n",
     "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
     "inputs = processor(text=prompt, images=[image1], return_tensors=\"pt\")\n",
     "inputs = inputs.to(device)\n",
     "\n",
-    "# Generate outputs\n",
+    "# Tạo đầu ra\n",
     "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
     "generated_texts = processor.batch_decode(\n",
     "    generated_ids,\n",
     "    skip_special_tokens=True,\n",
     ")\n",
     "\n",
-    "print(generated_texts)\n"
+    "print(generated_texts)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2. Comparing Multiple Images\n",
-    "The model can process and compare multiple images. Let's determine the common theme between two images."
+    "### 2. So sánh nhiều hình ảnh\n",
+    "Mô hình có thể xử lý và so sánh nhiều hình ảnh. Hãy xác định chủ đề chung giữa hai hình ảnh."
    ]
   },
   {
@@ -200,18 +204,17 @@
     }
    ],
    "source": [
-    "\n",
-    "# Load images\n",
+    "# Tải hình ảnh\n",
     "image2 = load_image(image_url2)\n",
     "\n",
-    "# Create input messages\n",
+    "# Tạo các thông báo đầu vào\n",
     "messages = [\n",
     "    # {\n",
     "    #     \"role\": \"user\",\n",
     "    #     \"content\": [\n",
     "    #         {\"type\": \"image\"},\n",
     "    #         {\"type\": \"image\"},\n",
-    "    #         {\"type\": \"text\", \"text\": \"Can you describe the two images?\"}\n",
+    "    #         {\"type\": \"text\", \"text\": \"Bạn có thể mô tả hai hình ảnh này không?\"}\n",
     "    #     ]\n",
     "    # },\n",
     "    {\n",
@@ -219,17 +222,17 @@
     "        \"content\": [\n",
     "            {\"type\": \"image\"},\n",
     "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"What event do they both represent?\"}\n",
+    "            {\"type\": \"text\", \"text\": \"Chúng đại diện cho sự kiện gì?\"}\n",
     "        ]\n",
     "    },\n",
     "]\n",
     "\n",
-    "# Prepare inputs\n",
+    "# Chuẩn bị đầu vào\n",
     "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
     "inputs = processor(text=prompt, images=[image1, image2], return_tensors=\"pt\")\n",
     "inputs = inputs.to(device)\n",
     "\n",
-    "# Generate outputs\n",
+    "# Tạo đầu ra\n",
     "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
     "generated_texts = processor.batch_decode(\n",
     "    generated_ids,\n",
@@ -243,9 +246,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 🔠 Text Recognition (OCR)\n",
-    "VLM can also recognize and interpret text in images, making it suitable for tasks like document analysis.\n",
-    "You could try experimenting on images with denser text."
+    "### 🔠 Nhận dạng văn bản (OCR)\n",
+    "VLM cũng có thể nhận ra và diễn giải văn bản trong hình ảnh, làm cho nó phù hợp với các tác vụ như phân tích tài liệu.\n",
+    "Bạn có thể thử nghiệm trên các hình ảnh có văn bản dày đặc hơn."
    ]
   },
   {
@@ -277,26 +280,26 @@
     "document_image_url = \"https://cdn.pixabay.com/photo/2020/11/30/19/23/christmas-5792015_960_720.png\"\n",
     "display(Image(url=document_image_url))\n",
     "\n",
-    "# Load the document image\n",
+    "# Tải hình ảnh tài liệu\n",
     "document_image = load_image(document_image_url)\n",
     "\n",
-    "# Create input message for analysis\n",
+    "# Tạo thông báo đầu vào để phân tích\n",
     "messages = [\n",
     "    {\n",
     "        \"role\": \"user\",\n",
     "        \"content\": [\n",
     "            {\"type\": \"image\"},\n",
-    "            {\"type\": \"text\", \"text\": \"What is written?\"}\n",
+    "            {\"type\": \"text\", \"text\": \"Nội dung là gì?\"}\n",
     "        ]\n",
     "    }\n",
     "]\n",
     "\n",
-    "# Prepare inputs\n",
+    "# Chuẩn bị đầu vào\n",
     "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
     "inputs = processor(text=prompt, images=[document_image], return_tensors=\"pt\")\n",
     "inputs = inputs.to(device)\n",
     "\n",
-    "# Generate outputs\n",
+    "# Tạo đầu ra\n",
     "generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
     "generated_texts = processor.batch_decode(\n",
     "    generated_ids,\n",
@@ -310,14 +313,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Processing videos\n",
+    "## Xử lý video\n",
+    "Các Mô hình Ngôn ngữ Thị giác (VLM) có thể xử lý video gián tiếp bằng cách trích xuất các khung hình chính (keyframe) và suy luận trên chúng theo thứ tự thời gian. Mặc dù VLM thiếu khả năng nhận biết thời gian thực của các mô hình video chuyên dụng, chúng vẫn có thể:\n",
+    "\n",
+    "- Mô tả các hành động hoặc sự kiện bằng cách phân tích các khung hình được lấy mẫu tuần tự.\n",
+    "\n",
+    "- Trả lời các câu hỏi về video dựa trên các khung hình chính đại diện.\n",
     "\n",
-    "Visual-Language Models (VLMs) can process videos indirectly by extracting keyframes and reasoning over them in temporal order. While VLMs lack the temporal modeling capabilities of dedicated video models, they can still:\n",
-    "- Describe actions or events by analyzing sampled frames sequentially.\n",
-    "- Answer questions about videos based on representative keyframes.\n",
-    "- Summarize video content by combining textual descriptions of multiple frames.\n",
+    "- Tóm tắt nội dung video bằng cách kết hợp các mô tả văn bản của nhiều khung hình.\n",
     "\n",
-    "Let experiment on an example:\n",
+    "Hãy thử nghiệm trên một ví dụ:\n",
     "\n",
     "<video width=\"640\" height=\"360\" controls>\n",
     "  <source src=\"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\" type=\"video/mp4\">\n",
@@ -347,7 +352,7 @@
     "def extract_frames(video_path, max_frames=50, target_size=None):\n",
     "    cap = cv2.VideoCapture(video_path)\n",
     "    if not cap.isOpened():\n",
-    "        raise ValueError(f\"Could not open video: {video_path}\")\n",
+    "        raise ValueError(f\"Không thể mở video: {video_path}\")\n",
     "    \n",
     "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
     "    frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int)\n",
@@ -373,7 +378,7 @@
     "    top = (image.height - target_size) // 2\n",
     "    return image.crop((left, top, left + target_size, top + target_size))\n",
     "\n",
-    "# Video link\n",
+    "# Đường dẫn video\n",
     "video_link = \"https://cdn.pixabay.com/video/2023/10/28/186794-879050032_large.mp4\""
    ]
   },
@@ -392,7 +397,7 @@
     }
    ],
    "source": [
-    "question = \"Describe what the woman is doing.\"\n",
+    "question = \"Mô tả những gì người phụ nữ đang làm.\"\n",
     "\n",
     "def generate_response(model, processor, frames, question):\n",
     "\n",
@@ -400,7 +405,7 @@
     "    messages = [\n",
     "        {\n",
     "            \"role\": \"user\",\n",
-    "            \"content\": [{\"type\": \"text\", \"text\": \"Following are the frames of a video in temporal order.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": \"Sau đây là các khung hình của một video theo thứ tự thời gian.\"}, *image_tokens, {\"type\": \"text\", \"text\": question}]\n",
     "        }\n",
     "    ]\n",
     "    inputs = processor(\n",
@@ -414,34 +419,34 @@
     "    )\n",
     "    return processor.decode(outputs[0], skip_special_tokens=True)\n",
     "\n",
-    "\n",
-    "# Extract frames from the video\n",
+    "# Trích xuất khung hình từ video\n",
     "frames = extract_frames(video_link, max_frames=15, target_size=384)\n",
     "\n",
     "processor.image_processor.size = (384, 384)\n",
     "processor.image_processor.do_resize = False\n",
-    "# Generate response\n",
+    "# Tạo phản hồi\n",
     "response = generate_response(model, processor, frames, question)\n",
     "\n",
-    "# Display the result\n",
-    "# print(\"Question:\", question)\n",
-    "print(\"Response:\", response)"
+    "# Hiển thị kết quả\n",
+    "# print(\"Câu hỏi:\", question)\n",
+    "print(\"Phản hồi:\", response)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💐 You're Done!\n",
+    "## 💐 Bạn đã hoàn thành!\n",
+    "Notebook này đã minh họa cách sử dụng Mô hình Ngôn ngữ Thị giác (VLM) như định dạng lời nhắc cho các tác vụ đa phương thức. Bằng cách làm theo các bước được nêu ở đây, bạn có thể thử nghiệm với VLM và các ứng dụng của chúng.\n",
+    "\n",
+    "### Các bước tiếp theo để khám phá:\n",
+    "- Thử nghiệm với nhiều trường hợp sử dụng VLM hơn.\n",
     "\n",
-    "This notebook demonstrated how to use a Visual-Language Model (VLM) such as formating prompts for multimodal tasks. By following the steps outlined here, you can experiment with VLMs and their applications.\n",
+    "- Cộng tác với đồng nghiệp bằng cách xem xét các yêu cầu kéo (PR) của họ.\n",
     "\n",
-    "### Next Steps to Explore:\n",
-    "- Experiment with more use cases of VLMs.\n",
-    "- Collaborate with a colleague by reviewing their pull requests (PRs).\n",
-    "- Contribute to improving this course material by opening an issue or submitting a PR to introduce new use cases, examples, or concepts.\n",
+    "- Đóng góp để cải thiện tài liệu khóa học này bằng cách mở một vấn đề (issue) hoặc gửi PR để giới thiệu các trường hợp sử dụng, ví dụ hoặc khái niệm mới.\n",
     "\n",
-    "Happy exploring! 🌟"
+    "Chúc bạn khám phá vui vẻ! 🌟"
    ]
   }
  ],

From 82b6e35920f693dfd8292409db87f557ac53ee0c Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 22 Jan 2025 20:56:30 +0100
Subject: [PATCH 56/63] init

---
 vi/6_synthetic_datasets/README.md | 38 +++++++++++++++----------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/vi/6_synthetic_datasets/README.md b/vi/6_synthetic_datasets/README.md
index 57e0d92a..fe933bd4 100644
--- a/vi/6_synthetic_datasets/README.md
+++ b/vi/6_synthetic_datasets/README.md
@@ -1,39 +1,39 @@
-# Synthetic Datasets
+# Tạo tập dữ liệu giả lập (Synthetic Datasets)
 
-Synthetic data is artificially generated data that mimics real-world usage. It allows overcoming data limitations by expanding or enhancing datasets. Even though synthetic data was already used for some use cases, large language models have made synthetic datasets more popular for pre- and post-training, and the evaluation of language models.
+Dữ liệu giả lập (synthetic data) là dữ liệu được tạo ra nhân tạo mô phỏng việc sử dụng trong thế giới thực. Nó cho phép khắc phục các hạn chế về dữ liệu bằng cách mở rộng hoặc nâng cao các tập dữ liệu. Mặc dù dữ liệu giả lập đã được sử dụng cho một số trường hợp, các mô hình ngôn ngữ lớn đã làm cho các tập dữ liệu giả lập trở nên phổ biến hơn cho việc huấn luyện trước, huấn luyện sau và đánh giá các mô hình ngôn ngữ.
 
-We'll use [`distilabel`](https://distilabel.argilla.io/latest/), a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers. For a deeper dive into the package and best practices, check out the [documentation](https://distilabel.argilla.io/latest/).
+Chúng ta sẽ sử dụng [`distilabel`](https://distilabel.argilla.io/latest/), một thư viện (framework) tạo dữ liệu giả lập và phản hồi AI cho các kỹ sư, những người cần các quy trình (pipeline) nhanh, đáng tin cậy và có thể mở rộng dựa trên các bài báo nghiên cứu đã được xác minh. Để tìm hiểu sâu hơn về package và các phương pháp hay nhất, hãy xem [tài liệu](https://distilabel.argilla.io/latest/).
 
-## Module Overview
+## Tổng quan về Mô-đun
 
-Synthetic data for language models can be categorized into three taxonomies: instructions, preferences and critiques. We will focus on the first two categories, which focus on the generation of datasets for instruction tuning and preference alignment. In both categories, we will cover aspects of the third category, which focuses on improving existing data with model critiques and rewrites.
+Dữ liệu giả lập cho các mô hình ngôn ngữ có thể được phân loại thành ba loại: hướng dẫn (instructions), sở thích (preferences) và phê bình (critiques). Chúng ta sẽ tập trung vào hai loại đầu tiên, tập trung vào việc tạo ra các tập dữ liệu để tinh chỉnh hướng dẫn (instruction tuning) và điều chỉnh sở thích (preference alignment). Trong cả hai loại, chúng ta sẽ đề cập đến các khía cạnh của loại thứ ba, tập trung vào việc cải thiện dữ liệu hiện có bằng các phê bình và viết lại của mô hình.
 
-![Synthetic Data Taxonomies](./images/taxonomy-synthetic-data.png)
+![Phân loại dữ liệu giả lập](./images/taxonomy-synthetic-data.png)
 
-## Contents
+## Nội dung
 
-### 1. [Instruction Datasets](./instruction_datasets.md)
+### 1. [Tập dữ liệu hướng dẫn](./instruction_datasets.md)
 
-Learn how to generate instruction datasets for instruction tuning. We will explore creating instruction tuning datasets thorugh basic prompting and using prompts more refined techniques from papers. Instruction tuning datasets with seed data for in-context learning can be created through methods like SelfInstruct and Magpie. Additionally, we will explore instruction evolution through EvolInstruct. [Start learning](./instruction_datasets.md).
+Tìm hiểu cách tạo tập dữ liệu hướng dẫn để tinh chỉnh hướng dẫn. Chúng ta sẽ khám phá việc tạo các tập dữ liệu tinh chỉnh hướng dẫn thông qua các lời nhắc (prompting) cơ bản và sử dụng các kỹ thuật nhắc nhở tinh tế hơn từ các bài báo. Các tập dữ liệu tinh chỉnh hướng dẫn với dữ liệu mẫu (seed data) để học trong ngữ cảnh (in-context learning) có thể được tạo ra thông qua các phương pháp như `SelfInstruct` và `Magpie`. Ngoài ra, chúng ta sẽ khám phá sự tiến hóa hướng dẫn thông qua `EvolInstruct`. [Bắt đầu học](./instruction_datasets.md).
 
-### 2. [Preference Datasets](./preference_datasets.md)
+### 2. [Tập dữ liệu ưu tiên](./preference_datasets.md)
 
-Learn how to generate preference datasets for preference alignment. We will build on top of the methods and techniques introduced in section 1, by generating additional responses. Next, we will learn how to improve such responses with the EvolQuality prompt. Finally, we will explore how to evaluate responses with the the UltraFeedback prompt which will produce a score and critique, allowing us to create preference pairs. [Start learning](./preference_datasets.md).
+Tìm hiểu cách tạo tập dữ liệu sở thích để điều chỉnh sở thích. Chúng ta sẽ xây dựng dựa trên các phương pháp và kỹ thuật được giới thiệu trong phần 1, bằng cách tạo thêm các phản hồi. Tiếp theo, chúng ta sẽ học cách cải thiện các phản hồi đó bằng lời nhắc `EvolQuality`. Cuối cùng, chúng ta sẽ khám phá cách đánh giá các phản hồi bằng lời nhắc `UltraFeedback`, lời nhắc này sẽ tạo ra điểm số và phê bình, cho phép chúng ta tạo các cặp sở thích. [Bắt đầu học](./preference_datasets.md).
 
-### Exercise Notebooks
+### Notebook bài tập
 
-| Title | Description | Exercise | Link | Colab |
+| Tiêu đề | Mô tả | Bài tập | Liên kết | Colab |
 |-------|-------------|----------|------|-------|
-| Instruction Dataset | Generate a dataset for instruction tuning | 🐢 Generate an instruction tuning dataset <br> 🐕 Generate a dataset for instruction tuning with seed data <br> 🦁 Generate a dataset for instruction tuning with seed data and with instruction evolution | [Link](./notebooks/instruction_sft_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
-| Preference Dataset | Generate a dataset for preference alignment | 🐢 Generate a preference alignment dataset <br> 🐕 Generate a preference alignment dataset with response evolution <br> 🦁 Generate a preference alignment dataset with response evolution and critiques  | [Link](./notebooks/preference_alignment_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
+| Tập dữ liệu hướng dẫn | Tạo tập dữ liệu để tinh chỉnh hướng dẫn | 🐢 Tạo tập dữ liệu tinh chỉnh hướng dẫn <br> 🐕 Tạo tập dữ liệu tinh chỉnh hướng dẫn với dữ liệu hạt giống <br> 🦁 Tạo tập dữ liệu tinh chỉnh hướng dẫn với dữ liệu hạt giống và với sự tiến hóa hướng dẫn | [Liên kết](./notebooks/instruction_sft_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) |
+| Tập dữ liệu ưu tiên | Tạo tập dữ liệu để điều chỉnh sở thích | 🐢 Tạo tập dữ liệu điều chỉnh sở thích <br> 🐕 Tạo tập dữ liệu điều chỉnh sở thích với sự tiến hóa phản hồi <br> 🦁 Tạo tập dữ liệu điều chỉnh sở thích với sự tiến hóa phản hồi và phê bình | [Liên kết](./notebooks/preference_alignment_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) |
 
-## Resources
+## Tài liệu tham khảo
 
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
-- [Synthetic Data Generator is UI app](https://huggingface.co/blog/synthetic-data-generator)
+- [Tài liệu Distilabel](https://distilabel.argilla.io/latest/)
+- [Trình tạo dữ liệu tổng hợp là ứng dụng UI](https://huggingface.co/blog/synthetic-data-generator)
 - [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
 - [Self-instruct](https://arxiv.org/abs/2212.10560)
 - [Evol-Instruct](https://arxiv.org/abs/2304.12244)
 - [Magpie](https://arxiv.org/abs/2406.08464)
 - [UltraFeedback](https://arxiv.org/abs/2310.01377)
-- [Deita](https://arxiv.org/abs/2312.15685)
+- [Deita](https://arxiv.org/abs/2312.15685)
\ No newline at end of file

From 1ba2cd194d92c453e10df95f356394247e510227 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 22 Jan 2025 21:22:28 +0100
Subject: [PATCH 57/63] init

---
 .../instruction_datasets.md                   | 135 +++++++++---------
 1 file changed, 67 insertions(+), 68 deletions(-)

diff --git a/vi/6_synthetic_datasets/instruction_datasets.md b/vi/6_synthetic_datasets/instruction_datasets.md
index c909ee64..228a586e 100644
--- a/vi/6_synthetic_datasets/instruction_datasets.md
+++ b/vi/6_synthetic_datasets/instruction_datasets.md
@@ -1,16 +1,16 @@
-# Generating Instruction Datasets
+# Tạo tập dữ liệu hướng dẫn
 
-Within [the chapter on instruction tuning](../1_instruction_tuning/README.md), we learned about fine-tuning models with Supervised Fine-tuning. In this section, we will explore how to generate instruction datasets for SFT. We will explore creating instruction tuning datasets through basic prompting and using more refined techniques from papers. Instruction tuning datasets with seed data for in-context learning can be created through methods like SelfInstruct and Magpie. Additionally, we will explore instruction evolution through EvolInstruct. Lastly, we will explore how to generate a dataset for instruction tuning using a distilabel pipeline.
+Trong [chương về tinh chỉnh hướng dẫn (instruction tuning)](../1_instruction_tuning/README.md), chúng ta đã học về việc tinh chỉnh mô hình với Tinh chỉnh có giám sát (Supervised Fine-tuning). Trong phần này, chúng ta sẽ khám phá cách tạo tập dữ liệu hướng dẫn cho SFT. Chúng ta sẽ khám phá việc tạo các tập dữ liệu tinh chỉnh hướng dẫn thông qua việc nhắc nhở (prompting) cơ bản và sử dụng các kỹ thuật tinh tế hơn từ các bài báo. Các tập dữ liệu tinh chỉnh hướng dẫn với dữ liệu hạt giống (seed data) để học trong ngữ cảnh (in-context learning) có thể được tạo ra thông qua các phương pháp như SelfInstruct và Magpie. Ngoài ra, chúng ta sẽ khám phá sự tiến hóa hướng dẫn thông qua EvolInstruct. Cuối cùng, chúng ta sẽ khám phá cách tạo tập dữ liệu để tinh chỉnh hướng dẫn bằng cách sử dụng quy trình (pipeline) distilabel.
 
-## From Prompt to Data
+## Từ lời nhắc đến dữ liệu
 
-Synthetic data sounds fancy, but it can be simplified as creating data through effective prompting to extract knowledge from a model. In turn, you can think of this as a way to generate data for a specific task. The challenge is prompting effectively while ensuring the data is diverse and representative. Fortunately, many papers have explored this problem, and we will explore some of the useful ones during this course. First things first, we will explore how to generate synthetic data through manual prompting.
+Dữ liệu giả lập (Synthetic data) nghe có vẻ phức tạp, nhưng nó có thể được đơn giản hóa thành việc tạo dữ liệu thông qua việc nhắc nhở hiệu quả để trích xuất kiến thức từ mô hình. Đổi lại, bạn có thể coi đây là một cách để tạo dữ liệu cho một tác vụ cụ thể. Thách thức là nhắc nhở một cách hiệu quả trong khi đảm bảo dữ liệu đa dạng và mang tính đại diện. May mắn thay, nhiều bài báo đã khám phá vấn đề này và chúng ta sẽ khám phá một số bài báo hữu ích trong khóa học này. Trước hết, chúng ta sẽ khám phá cách tạo dữ liệu giả lập thông qua việc nhắc nhở thủ công.
 
-### Basic Prompting
+### Nhắc nhở cơ bản (Basic Prompting)
 
-Let's start with a basic example and load the [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) model using the `transformers` integration of the `distilabel` library. We will use the `TextGeneration` class to generate a synthetic `prompt` and use that to generate a `completion`.
+Hãy bắt đầu với một ví dụ cơ bản và tải mô hình [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) bằng cách sử dụng tích hợp `transformers` của thư viện `distilabel`. Chúng ta sẽ sử dụng lớp `TextGeneration` để tạo ra một `lời nhắc` (prompt) tổng hợp và sử dụng nó để tạo ra một `phần hoàn thành` (completion).
 
-Next, we will load the model using the `distilabel` library.
+Tiếp theo, chúng ta sẽ tải mô hình bằng thư viện `distilabel`.
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -21,40 +21,40 @@ gen = TextGeneration(llm=llm)
 gen.load()
 ```
 
-!!! note
-    Distilabel loads the `llm` into memory, so, when working in a notebook, we need to `gen.unload()` after we are done with it to avoid memory issues.
+> **Note:**  
+> `Distilabel` tải `llm` vào bộ nhớ, vì vậy, khi làm việc trong notebook, chúng ta cần `gen.unload()` sau khi hoàn thành để tránh các vấn đề về bộ nhớ.
 
-We will now use the `llm` to generate a `prompt` for instruction tuning.
+Bây giờ chúng ta sẽ sử dụng `llm` để tạo ra một `lời nhắc` để tinh chỉnh hướng dẫn.
 
 ```python
-next(gen.process([{"instruction": "Generate a questions about the Hugging Face Smol-Course on small AI models."}]))
-# What is the purpose of Smol-Course?
+next(gen.process([{"instruction": "Tạo một câu hỏi về Khóa học Smol của Hugging Face về các mô hình AI nhỏ."}]))
+# Ví dụ: Mục đích của Khóa học Smol là gì?
 ```
 
-Lastly, we can use that same `prompt` as input to generate a `completion`.
+Cuối cùng, chúng ta có thể sử dụng cùng một `lời nhắc` đó làm đầu vào để tạo ra một `phần hoàn thành`.
 
 ```python
-next(gen.process([{"instruction": "What is the purpose of Smol-Course?"}]))
-# The Smol-Course is a platform designed to learning computer science concepts.
+next(gen.process([{"instruction": "Mục đích của Khóa học Smol là gì?"}]))
+# Ví dụ: Khóa học Smol là một nền tảng được thiết kế để học các khái niệm khoa học máy tính.
 ```
 
-Cool! We can generated a synthetic `prompt` and a corresponding `completion`. Re-using this simple approach at scale will allow us to generate a lot more data however, the quality of the data is not that great and does not take into account the nuances of our course or domain. Additionally, re-running the current code shows us the data is not that diverse. Luckily, there are ways to solve this problem.
+Tuyệt! Chúng ta có thể tạo ra một `lời nhắc` tổng hợp và một `phần hoàn thành` tương ứng. Việc sử dụng lại phương pháp đơn giản này trên quy mô lớn sẽ cho phép chúng ta tạo ra nhiều dữ liệu hơn, tuy nhiên, chất lượng của dữ liệu không tốt lắm và không tính đến các sắc thái của khóa học hoặc lĩnh vực của chúng ta. Ngoài ra, việc chạy lại mã hiện tại cho chúng ta thấy dữ liệu không đa dạng lắm. May mắn thay, có nhiều cách để giải quyết vấn đề này.
 
 ### SelfInstruct
 
-SelfInstruct is a prompt that generates new instructions based on a seed dataset. This seed data can be a single instruction or a piece of context. The process begins with a pool of initial seed data. The language model is then prompted to generate new instructions based on this seed data using in-context learning. The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) and a simplified version is shown below:
+`SelfInstruct` là một lời nhắc tạo ra các hướng dẫn mới dựa trên tập dữ liệu mẫu. Dữ liệu mẫu này có thể là một hướng dẫn đơn lẻ hoặc một đoạn ngữ cảnh. Quá trình bắt đầu với một nhóm dữ liệu mẫu ban đầu. Mô hình ngôn ngữ sau đó được nhắc để tạo ra các hướng dẫn mới dựa trên dữ liệu mẫu này bằng cách sử dụng phương pháp học trong ngữ cảnh (in-context learning). Lời nhắc được [triển khai trong distilabel](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) và một phiên bản đơn giản hóa được hiển thị bên dưới:
 
 ```
-# Task Description
-Develop {{ num_instructions }} user queries that can be received by the given AI application and applicable to the provided context. Emphasize diversity in verbs and linguistic structures within the model's textual capabilities.
+# Mô tả nhiệm vụ
+Phát triển {{ num_instructions }} truy vấn của người dùng có thể được nhận bởi ứng dụng AI đã cho và áp dụng cho ngữ cảnh được cung cấp. Nhấn mạnh sự đa dạng trong động từ và cấu trúc ngôn ngữ trong khả năng văn bản của mô hình.
 
-# Context
+# Ngữ cảnh
 {{ input }}
 
-# Output
+# Đầu ra
 ```
 
-To use it, we need to pass the `llm` to the [SelfInstruct class](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/). Let's use the text from the [Prompt to Data section](#prompt-to-data) as context and generate a new instruction.
+Để sử dụng nó, chúng ta cần truyền `llm` cho [lớp SelfInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/). Hãy sử dụng văn bản từ [phần Từ lời nhắc đến dữ liệu](#prompt-to-data) làm ngữ cảnh và tạo ra một hướng dẫn mới.
 
 ```python
 from distilabel.steps.tasks import SelfInstruct
@@ -62,31 +62,31 @@ from distilabel.steps.tasks import SelfInstruct
 self_instruct = SelfInstruct(llm=llm)
 self_instruct.load()
 
-context = "<prompt_to_data_section>"
+context = "<prompt_to_data_section>" # Thay thế bằng nội dung của phần Từ lời nhắc đến dữ liệu
 
 next(self_instruct.process([{"input": text}]))["instructions"][0]
-# What is the process of generating synthetic data through manual prompting?
+# Quá trình tạo dữ liệu tổng hợp thông qua việc nhắc nhở thủ công là gì?
 ```
 
-The generated instruction is a lot better already and it fits our actual content and domain. However, we can do even better by improving the prompt through evolution.
+Hướng dẫn được tạo ra đã tốt hơn rất nhiều và nó phù hợp với nội dung và lĩnh vực thực tế của chúng ta. Tuy nhiên, chúng ta có thể làm tốt hơn nữa bằng cách cải thiện lời nhắc thông qua phương pháp tiến hóa (evolution).
 
 ### EvolInstruct
 
-EvolInstruct is a prompting technique that takes an input instruction and evolves it into a better version of the same instruction. This better version is defined according to a set of criteria and adds constraints, deepening, concretizing, reasoning or complications to the original instruction. The process can be repeated multiple times to create various evolutions of the same instruction, ideally leading to a better version of the original instruction. The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct) and a simplified version is shown below:
+EvolInstruct là một kỹ thuật nhắc nhở lấy một hướng dẫn đầu vào và phát triển nó thành một phiên bản tốt hơn của cùng một hướng dẫn. Phiên bản tốt hơn này được định nghĩa theo một tập hợp các tiêu chí và bổ sung các ràng buộc, đào sâu, cụ thể hóa, lập luận hoặc phức tạp hóa cho hướng dẫn ban đầu. Quá trình này có thể được lặp lại nhiều lần để tạo ra các phiên bản tiến hóa khác nhau của cùng một hướng dẫn, lý tưởng nhất là dẫn đến một phiên bản tốt hơn của hướng dẫn ban đầu. Lời nhắc được [triển khai trong distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct) và một phiên bản đơn giản hóa được hiển thị bên dưới:
 
 ```
-I want you act as a Prompt Rewriter.
-Given prompt a prompt, rewrite it into a more complex version.
-Complicate the prompt based on the following criteria:
+Tôi muốn bạn đóng vai trò là một Trình viết lại lời nhắc (Prompt Rewriter).
+Cho một lời nhắc, hãy viết lại nó thành một phiên bản phức tạp hơn.
+Phức tạp hóa lời nhắc dựa trên các tiêu chí sau:
 {{ criteria }}
 
-# Prompt
+# Lời nhắc
 {{ input }}
 
-# Output
+# Đầu ra
 ```
 
-To use it, we need to pass the `llm` to the [EvolInstruct class](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/). Let's use the synthetic prompt from [the SelfInstruct section](#selfinstruct) as input and evolve it into a better version. For this example, we will only evolve for one generation.
+Để sử dụng nó, chúng ta cần truyền `llm` cho [lớp EvolInstruct](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/). Hãy sử dụng lời nhắc tổng hợp từ [phần SelfInstruct](#selfinstruct) làm đầu vào và phát triển nó thành một phiên bản tốt hơn. Đối với ví dụ này, chúng ta sẽ chỉ tiến hóa trong một thế hệ.
 
 ```python
 from distilabel.steps.tasks import EvolInstruct
@@ -94,34 +94,34 @@ from distilabel.steps.tasks import EvolInstruct
 evol_instruct = EvolInstruct(llm=llm, num_evolutions=1)
 evol_instruct.load()
 
-text = "What is the process of generating synthetic data through manual prompting"
+text = "Quá trình tạo dữ liệu tổng hợp thông qua việc nhắc nhở thủ công là gì"
 
 next(evol_instruct.process([{"instruction": text}]))
-# What is the process of generating synthetic data through manual prompting?
-# And, how does the artificial intelligence system, GPT4, use machine learning algorithms to manipulate the input data into synthetic data?
+# Quá trình tạo dữ liệu tổng hợp thông qua việc nhắc nhở thủ công là gì?
+# Và, làm thế nào hệ thống trí tuệ nhân tạo, GPT4, sử dụng các thuật toán học máy để thao tác dữ liệu đầu vào thành dữ liệu tổng hợp?
 ```
 
-The instruction is now more complex but has lost some of the original meaning. So, take into account that evolving can be a double-edged sword and we need to be careful with the quality of the data we generate.
+Hướng dẫn bây giờ phức tạp hơn nhưng đã mất đi một số ý nghĩa ban đầu. Vì vậy, hãy lưu ý rằng việc tiến hóa có thể là một con dao hai lưỡi và chúng ta cần cẩn thận với chất lượng của dữ liệu chúng ta tạo ra.
 
 ### Magpie
 
-Magpie is a technique that relies on the auto-regressive factors of language model and the [chat-template](../1_instruction_tuning/chat_templates.md) that has been using during the instruction tuning process. As you might remember, the chat-template is a format that structures conversations with clear role indicators (system, user, assistant). During the instruction tuning phase, the language model has been optimized to reproduce this format and that is exactly what magpie takes advantage of. It starts with a pre-query-prompt based on the chat-template but it stops before the user message indicator, e.g. `<|im_start|>user\n`, and then it uses the language model to generate the user prompt until the end of the assistant indicator, e.g. `<|im_end|>`. This approach allows us to generate a lot of data in a very efficient way and it can even be scaled up to multi-turn conversations. It is hypothesized this generated data reproduces training data from the instruction tuning phase of the model used.
+Magpie là một kỹ thuật dựa vào các yếu tố tự suy luận (auto-regressive) của mô hình ngôn ngữ và [mẫu trò chuyện (chat-template)](../1_instruction_tuning/chat_templates.md) đã được sử dụng trong quá trình tinh chỉnh hướng dẫn. Như bạn có thể nhớ, mẫu trò chuyện là một định dạng cấu trúc các cuộc hội thoại với các chỉ số vai trò rõ ràng (hệ thống, người dùng, trợ lý). Trong giai đoạn tinh chỉnh hướng dẫn, mô hình ngôn ngữ đã được tối ưu hóa để tái tạo định dạng này và đó chính xác là những gì `Magpie` tận dụng. Nó bắt đầu với một lời nhắc trước truy vấn (pre-query-prompt) dựa trên mẫu trò chuyện nhưng nó dừng lại trước chỉ báo tin nhắn của người dùng, ví dụ: `<|im_start|>user\n`, và sau đó nó sử dụng mô hình ngôn ngữ để tạo ra lời nhắc của người dùng cho đến khi kết thúc chỉ báo trợ lý, ví dụ: `<|im_end|>`. Cách tiếp cận này cho phép chúng ta tạo ra rất nhiều dữ liệu một cách rất hiệu quả và thậm chí có thể mở rộng quy mô lên các cuộc hội thoại nhiều lượt. Người ta giả thuyết rằng dữ liệu được tạo ra này tái tạo dữ liệu huấn luyện từ giai đoạn tinh chỉnh hướng dẫn của mô hình được sử dụng.
 
-In this scenario, prompt templates differ per model because they are based on the chat-template format. But we can walk through a simplified version of the process step-by-step.
+Trong trường hợp này, các mẫu lời nhắc khác nhau cho mỗi mô hình vì chúng dựa trên định dạng mẫu trò chuyện. Nhưng chúng ta có thể đi qua một phiên bản đơn giản hóa của quá trình từng bước.
 
 ```bash
-# Step 1: provide the pre-query-prompt
+# Bước 1: cung cấp lời nhắc trước truy vấn
 <|im_start|>user\n
 
-# Step 2: the language model generates the user prompt
+# Bước 2: mô hình ngôn ngữ tạo ra lời nhắc của người dùng
 <|im_start|>user\n
-What is the purpose of Smol-Course?
+Mục đích của Khóa học Smol là gì?
 
-# Step 3: stop the generation
+# Bước 3: dừng quá trình tạo
 <|im_end|>
 ```
 
-To use it in distilabel, we need to pass the `llm` to the [Magpie class](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/).
+Để sử dụng nó trong distilabel, chúng ta cần truyền `llm` cho [lớp Magpie](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/).
 
 ```python
 from distilabel.steps.tasks import Magpie
@@ -129,35 +129,34 @@ from distilabel.steps.tasks import Magpie
 magpie = Magpie(llm=llm)
 magpie.load()
 
-next(magpie.process([{"system_prompt": "You are a helpful assistant."}]))
+next(magpie.process([{"system_prompt": "Bạn là một trợ lý hữu ích."}]))
 # [{
 #   "role": "user",
-#   "content": "Can you provide me with a list of the top 3 universities?"
+#   "content": "Bạn có thể cung cấp cho tôi danh sách 3 trường đại học hàng đầu không?"
 # },
 # {
 #   "role": "assistant",
-#   "content": "The top 3 universities are: MIT, Yale, Stanford."
+#   "content": "3 trường đại học hàng đầu là: MIT, Yale, Stanford."
 # }]
 ```
 
-
-We immediately get a dataset with a `prompt` and `completion` . To improve the performance on our own domain, we can inject additional context into the `system_prompt`. For the LLM to generate specific domain data in combination with Magpie, it helps describing in the system prompt what the users queries will be. This is then used in the  pre-query-prompt before we start generating the user prompt and bias the LLM to generate user queries of that domain.
+Chúng ta ngay lập tức nhận được một tập dữ liệu với một `lời nhắc` và `phần hoàn thành`. Để cải thiện hiệu suất trên lĩnh vực của riêng mình, chúng ta có thể đưa thêm ngữ cảnh vào `system_prompt`. Để LLM tạo ra dữ liệu lĩnh vực cụ thể kết hợp với Magpie, nó giúp mô tả trong lời nhắc hệ thống (system prompt) các truy vấn của người dùng sẽ là gì. Điều này sau đó được sử dụng trong lời nhắc trước truy vấn trước khi chúng ta bắt đầu tạo lời nhắc của người dùng và thiên về LLM để tạo ra các truy vấn của người dùng trong lĩnh vực đó.
 
 ```
-You're an AI assistant that will help users solving math problems.
+Bạn là một trợ lý AI sẽ giúp người dùng giải các bài toán.
 ```
 
-It's important to write the system prompt as shown above instead of something like:
+Điều quan trọng là phải viết lời nhắc hệ thống như được hiển thị ở trên thay vì một cái gì đó như:
 
 ```
-You're an AI assistant that generates math problems
+Bạn là một trợ lý AI tạo ra các bài toán
 ```
 
-Generally, language models are less optimized for passing additional context to the `system_prompt` so this does not always work as well for customisation as other techniques.
+Nói chung, các mô hình ngôn ngữ ít được tối ưu hóa hơn để truyền ngữ cảnh bổ sung cho `system_prompt` vì vậy điều này không phải lúc nào cũng hoạt động tốt cho việc tùy chỉnh như các kỹ thuật khác.
 
-### From Prompts to Pipelines
+### Từ lời nhắc đến quy trình (pipeline)
 
-The classes we've seen so far are all standalone classes that can be used in a pipeline. This is a good start, but we can do even better by using the `Pipeline` class to generate a dataset. We will use the `TextGeneration` step to generate a synthetic dataset for instruction tuning. The pipeline will consist of a `LoadDataFromDicts` step to load the data, a `TextGeneration` step to generate the `prompt` and a `completion` for that prompt. We will connect the steps and flow the data through the pipeline using the `>>` operator. Within the [documentation of distilabel](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/#input-output-columns) we can see input and output columns of the step. We to ensure that the data flow correctly through the pipeline, we will use the `output_mappings` parameter to map the output columns to the input columns of the next step.
+Các lớp chúng ta đã thấy cho đến nay đều là các lớp độc lập có thể được sử dụng trong một quy trình. Đây là một khởi đầu tốt, nhưng chúng ta có thể làm tốt hơn nữa bằng cách sử dụng lớp `Pipeline` để tạo tập dữ liệu. Chúng ta sẽ sử dụng bước `TextGeneration` để tạo tập dữ liệu tổng hợp để tinh chỉnh hướng dẫn. Quy trình sẽ bao gồm bước `LoadDataFromDicts` để tải dữ liệu, bước `TextGeneration` để tạo `lời nhắc` và `phần hoàn thành` cho lời nhắc đó. Chúng ta sẽ kết nối các bước và luồng dữ liệu thông qua quy trình bằng toán tử `>>`. Trong [tài liệu của distilabel](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/#input-output-columns), chúng ta có thể thấy các cột đầu vào và đầu ra của bước. Để đảm bảo rằng dữ liệu chảy chính xác qua quy trình, chúng ta sẽ sử dụng tham số `output_mappings` để ánh xạ các cột đầu ra với các cột đầu vào của bước tiếp theo.
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -166,7 +165,7 @@ from distilabel.steps import LoadDataFromDicts
 from distilabel.steps.tasks import TextGeneration
 
 with Pipeline() as pipeline:
-    data = LoadDataFromDicts(data=[{"instruction": "Generate a short question about the Hugging Face Smol-Course."}])
+    data = LoadDataFromDicts(data=[{"instruction": "Tạo một câu hỏi ngắn về Khóa học Smol của Hugging Face."}])
     llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
     gen_a = TextGeneration(llm=llm, output_mappings={"generation": "instruction"})
     gen_b = TextGeneration(llm=llm, output_mappings={"generation": "response"})
@@ -176,29 +175,29 @@ if __name__ == "__main__":
     distiset = pipeline.run(use_cache=False)
     print(distiset["default"]["train"][0])
 # [{
-#   "instruction": "What is the purpose of Smol-Course?",
-#   "response": "The Smol-Course is a platform designed to learning computer science concepts."
+#   "instruction": "Mục đích của Khóa học Smol là gì?",
+#   "response": "Khóa học Smol là một nền tảng được thiết kế để học các khái niệm khoa học máy tính."
 # }]
 ```
 
-Under the hood, this pipeline has a lot of cool features. It automatically caches generation results, so we can don't have to re-run the generation steps. There is included fault-tolerance, so if the generation steps fail, the pipeline will continue to run. And the pipeline exexutes all generation steps in parallel, so the generation is faster. We can even visualise the pipeline using the `draw` method. Here you can see how the data flows through the pipeline and how the `output_mappings` are used to map the output columns to the input columns of the next step.
+Bên dưới, quy trình này có rất nhiều tính năng hay. Nó tự động lưu trữ các kết quả tạo, vì vậy chúng ta không phải chạy lại các bước tạo. Thư viện có tích hợp khả năng xử lý lỗi (fault-tolerance), vì vậy nếu các bước tạo thất bại, quy trình vẫn sẽ tiếp tục chạy. Và quy trình thực hiện tất cả các bước tạo song song, vì vậy việc tạo nhanh hơn. Chúng ta thậm chí có thể trực quan hóa quy trình bằng phương thức `draw`. Ở đây bạn có thể thấy cách dữ liệu chảy qua quy trình và cách `output_mappings` được sử dụng để ánh xạ các cột đầu ra với các cột đầu vào của bước tiếp theo.
 
 ![Pipeline](./images/pipeline.png)
 
-## Best Practices
+## Các phương pháp hay nhất
 
-- Ensure you have a diverse seed data to cover a wide range of scenarios
-- Regularly evaluate the dataset to ensure generated data is diverse and of high quality
-- Iterate on the (system)prompt to improve the quality of the data
+- Đảm bảo bạn có dữ liệu hạt giống đa dạng để bao quát nhiều tình huống
+- Thường xuyên đánh giá tập dữ liệu để đảm bảo dữ liệu được tạo ra đa dạng và có chất lượng cao
+- Lặp lại trên (system)prompt để cải thiện chất lượng của dữ liệu
 
-## Next Steps
+## Các bước tiếp theo
 
-👨🏽‍💻 Code -[Exercise Notebook](./notebooks/instruction_sft_dataset.ipynb) to generate a dataset for instruction tuning
-🧑‍🏫 Learn - About [generating preference datasets](./preference_datasets.md)
+👨🏽‍💻 Lập trình -[Notebook bài tập](./notebooks/instruction_sft_dataset.ipynb) để tạo tập dữ liệu để tinh chỉnh hướng dẫn
+🧑‍🏫 Tìm hiểu - Về [tạo tập dữ liệu sở thích](./preference_datasets.md)
 
-## References
+## Tài liệu tham khảo
 
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Tài liệu Distilabel](https://distilabel.argilla.io/latest/)
 - [Self-instruct](https://arxiv.org/abs/2212.10560)
 - [Evol-Instruct](https://arxiv.org/abs/2304.12244)
-- [Magpie](https://arxiv.org/abs/2406.08464)
\ No newline at end of file
+- [Magpie](https://arxiv.org/abs/2406.08464)

From 5ba100b1e0effca7d969c0b9a817ea21a6a102f9 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 22 Jan 2025 21:38:42 +0100
Subject: [PATCH 58/63] init

---
 .../preference_datasets.md                    | 102 +++++++++---------
 1 file changed, 51 insertions(+), 51 deletions(-)

diff --git a/vi/6_synthetic_datasets/preference_datasets.md b/vi/6_synthetic_datasets/preference_datasets.md
index 38bb0e02..40af3373 100644
--- a/vi/6_synthetic_datasets/preference_datasets.md
+++ b/vi/6_synthetic_datasets/preference_datasets.md
@@ -1,16 +1,16 @@
-# Generating Preference Datasets
+# Tạo tập dữ liệu ưu tiên (Preference Datasets)
 
-Within [the chapter on preference alignment](../2_preference_alignment/README.md), we learned about Direct Preference Optimization. In this section, we will explore how to generate preference datasets for methods like DPO. We will build on top of the methods that were introduced in [generating instruction datasets](./instruction_datasets.md). Additionally, we will show how to add extra completions to the dataset using basic prompting or by using EvolQuality to improve the quality of responses. Lastly, we will show how UltraFeedback can be used to generate scores and critiques.
+Trong [chương về điều chỉnh ưu tiên (preference alignment)](../2_preference_alignment/README.md), chúng ta đã học về Tối ưu hóa ưu tiên trực tiếp (Direct Preference Optimization). Trong phần này, chúng ta sẽ khám phá cách tạo tập dữ liệu ưu tiên cho các phương pháp như DPO. Chúng ta sẽ xây dựng dựa trên các phương pháp đã được giới thiệu trong phần [tạo tập dữ liệu hướng dẫn](./instruction_datasets.md). Ngoài ra, chúng ta sẽ chỉ ra cách thêm các phần hoàn thành (completions) bổ sung vào tập dữ liệu bằng cách sử dụng kỹ thuật nhắc nhở (prompting) cơ bản hoặc bằng cách sử dụng EvolQuality để cải thiện chất lượng của các phản hồi. Cuối cùng, chúng ta sẽ chỉ ra cách `UltraFeedback` có thể được sử dụng để tạo điểm số và phê bình.
 
-## Creating multiple completions
+## Tạo nhiều phần hoàn thành (completions)
 
-Preference data is a dataset with multiple `completions` for the same `instruction`. We can add more `completions` to a dataset by prompting a model to generate them. When doing this, we need to ensure that the second completion is not too similar to the first completion in terms of overall quality and phrasing. This is important because the model needs to be optimized for a clear preference. We want to know which completion is preferred over the other, normally referred to as `chosen` and `rejected`. We will go into more detail about determining chosen and rejected completions in the [section on creating scores](#creating-scores).
+Dữ liệu ưu tiên là tập dữ liệu có nhiều `phần hoàn thành` cho cùng một `hướng dẫn`. Chúng ta có thể thêm nhiều `phần hoàn thành` hơn vào tập dữ liệu bằng cách nhắc nhở (prompt) một mô hình tạo ra chúng. Khi làm điều này, chúng ta cần đảm bảo rằng phần hoàn thành thứ hai không quá giống với phần hoàn thành đầu tiên về chất lượng tổng thể và cách diễn đạt. Điều này rất quan trọng vì mô hình cần được tối ưu hóa cho một ưu tiên rõ ràng. Chúng ta muốn biết phần hoàn thành nào được ưa thích hơn phần kia, thường được gọi là `chosen` (được chọn) và `rejected` (bị từ chối). Chúng ta sẽ đi vào chi tiết hơn về việc xác định các phần hoàn thành được chọn và bị từ chối trong [phần tạo điểm số](#creating-scores).
 
-### Model pooling
+### Tổng hợp mô hình (Model pooling)
 
-You can use models from different model families to generate a second completion, which is called model pooling. To further improve the quality of the second completion, you can use different generation arguments, like tweaking the `temperature`. Lastly, you can use different prompt templates or system prompts to generate a second completion to ensure diversity based on specific characteristics defined in the template. In theory, we could take two models of varying quality and use the better one as the `chosen` completion.
+Bạn có thể sử dụng các mô hình từ các họ mô hình khác nhau để tạo phần hoàn thành thứ hai, được gọi là tổng hợp mô hình. Để cải thiện hơn nữa chất lượng của phần hoàn thành thứ hai, bạn có thể sử dụng các đối số tạo khác nhau, như điều chỉnh `temperature`. Cuối cùng, bạn có thể sử dụng các mẫu lời nhắc (prompt templates) hoặc lời nhắc hệ thống (system prompts) khác nhau để tạo phần hoàn thành thứ hai nhằm đảm bảo sự đa dạng dựa trên các đặc điểm cụ thể được xác định trong mẫu. Về lý thuyết, chúng ta có thể lấy hai mô hình có chất lượng khác nhau và sử dụng mô hình tốt hơn làm phần hoàn thành `chosen`.
 
-Let's start with model pooling by loading the [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) and [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) models using the `transformers` integration of the `distilabel` library. Using these models, we will create two synthetic `responses` for a given `prompt`. We will create another pipeline with `LoadDataFromDicts`, `TextGeneration`, and `GroupColumns`. We will first load data, then use two generation steps, and then group the results. We connect the steps and flow the data through the pipeline using the `>>` operator and `[]`, which means that we want to use the output of the previous step as the input for both steps within the list.
+Hãy bắt đầu với việc tổng hợp mô hình bằng cách tải các mô hình [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) và [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) bằng cách sử dụng tích hợp `transformers` của thư viện `distilabel`. Sử dụng các mô hình này, chúng ta sẽ tạo ra hai `phản hồi` tổng hợp cho một `lời nhắc` nhất định. Chúng ta sẽ tạo một quy trình (pipeline) khác với `LoadDataFromDicts`, `TextGeneration` và `GroupColumns`. Trước tiên, chúng ta sẽ tải dữ liệu, sau đó sử dụng hai bước tạo và sau đó nhóm các kết quả lại. Chúng ta kết nối các bước và luồng dữ liệu thông qua quy trình bằng toán tử `>>` và `[]`, có nghĩa là chúng ta muốn sử dụng đầu ra của bước trước làm đầu vào cho cả hai bước trong danh sách.
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -19,7 +19,7 @@ from distilabel.steps import GroupColumns, LoadDataFromDicts
 from distilabel.steps.tasks import TextGeneration
 
 with Pipeline() as pipeline:
-    data = LoadDataFromDicts(data=[{"instruction": "What is synthetic data?"}])
+    data = LoadDataFromDicts(data=[{"instruction": "Dữ liệu giả lập (synthetic data) là gì?"}])
     llm_a = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
     gen_a = TextGeneration(llm=llm_a)
     llm_b = TransformersLLM(model="Qwen/Qwen2.5-1.5B-Instruct")
@@ -31,35 +31,35 @@ if __name__ == "__main__":
     distiset = pipeline.run()
     print(distiset["default"]["train"]["grouped_generation"][0])
 # {[
-#   'Synthetic data is artificially generated data that mimics real-world usage.',
-#   'Synthetic data refers to data that has been generated artificially.'
+#   'Dữ liệu giả lập là dữ liệu được tạo ra nhân tạo, bắt chước cách sử dụng trong thế giới thực.',
+#   'Dữ liệu giả lập đề cập đến dữ liệu đã được tạo ra một cách nhân tạo.'
 # ]}
 ```
 
-As you can see, we have two synthetic `completions` for the given `prompt`. We could have boosted diversity by initializing the `TextGeneration` steps with a specific `system_prompt` or by passing generation arguments to the `TransformersLLM`. Let's now see how we can improve the quality of the `completions` using EvolQuality.
+Như bạn có thể thấy, chúng ta có hai `phần hoàn thành` tổng hợp cho `lời nhắc` đã cho. Chúng ta có thể tăng cường sự đa dạng bằng cách khởi tạo các bước `TextGeneration` với một `system_prompt` cụ thể hoặc bằng cách truyền các đối số tạo cho `TransformersLLM`. Bây giờ hãy xem cách chúng ta có thể cải thiện chất lượng của các `phần hoàn thành` bằng EvolQuality.
 
 ### EvolQuality
 
-EvolQuality is similar to [EvolInstruct](./instruction_datasets.md#evolinstruct) - it is a prompting technique but it evolves `completions` instead of the input `prompt`. The task takes both a `prompt` and `completion` and evolves the `completion` into a version that better responds to the `prompt` based on a set of criteria. This better version is defined according to criteria for improving helpfulness, relevance, deepening, creativity, or details. Because this automatically generates a second completion, we can use it to add more `completions` to a dataset. In theory, we could even assume the evolution is better than the original completion and use it as the `chosen` completion out of the box.
+EvolQuality tương tự như [EvolInstruct](./instruction_datasets.md#evolinstruct) - đó là một kỹ thuật nhắc nhở nhưng nó phát triển `các phần hoàn thành` thay vì `lời nhắc` đầu vào. Tác vụ lấy cả `lời nhắc` và `phần hoàn thành` và phát triển `phần hoàn thành` thành một phiên bản phản hồi tốt hơn cho `lời nhắc` dựa trên một tập hợp các tiêu chí. Phiên bản tốt hơn này được định nghĩa theo các tiêu chí để cải thiện tính hữu ích, mức độ liên quan, đào sâu, sáng tạo hoặc chi tiết. Bởi vì điều này tự động tạo ra phần hoàn thành thứ hai, chúng ta có thể sử dụng nó để thêm nhiều `phần hoàn thành` hơn vào tập dữ liệu. Về lý thuyết, chúng ta thậm chí có thể giả định rằng sự tiến hóa tốt hơn phần hoàn thành ban đầu và sử dụng nó làm phần hoàn thành `chosen` ngay lập tức.
 
-The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality) and a simplified version is shown below:
+Lời nhắc được [triển khai trong distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality) và phiên bản đơn giản hóa được hiển thị bên dưới:
 
 ```bash
-I want you act as a Response Rewriter.
-Given prompt a and a response, rewrite the response into a better version.
-Complicate the prompt based on the following criteria:
+Tôi muốn bạn đóng vai trò là một Trình viết lại phản hồi (Response Rewriter).
+Cho một lời nhắc và một phản hồi, hãy viết lại phản hồi thành một phiên bản tốt hơn.
+Phức tạp hóa lời nhắc dựa trên các tiêu chí sau:
 {{ criteria }}
 
-# Prompt
+# Lời nhắc
 {{ input }}
 
-# Response
+# Phản hồi
 {{ output }}
 
-# Improved Response
+# Phản hồi được cải thiện
 ```
 
-Let's use the [EvolQuality class](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) to evolve the synthetic `prompt` and `completion` from [the Model Pooling section](#model-pooling) into a better version. For this example, we will only evolve for one generation.
+Hãy sử dụng [lớp EvolQuality](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) để phát triển `lời nhắc` và `phần hoàn thành` tổng hợp từ [phần Tổng hợp mô hình](#model-pooling) thành một phiên bản tốt hơn. Đối với ví dụ này, chúng ta sẽ chỉ tiến hóa trong một thế hệ.
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -69,49 +69,49 @@ llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
 evol_quality = EvolQuality(llm=llm, num_evolutions=1)
 evol_quality.load()
 
-instruction = "What is synthetic data?"
-completion = "Synthetic data is artificially generated data that mimics real-world usage."
+instruction = "Dữ liệu giả lập (synthetic data) là gì?"
+completion = "Dữ liệu giả lập là dữ liệu được tạo ra nhân tạo, bắt chước cách sử dụng trong thế giới thực."
 
 next(evol_quality.process([{
     "instruction": instruction,
     "response": completion
 }]))
-# The process of generating synthetic data through manual prompting involves creating artificial data sets that mimic real-world usage patterns.
+# Quá trình tạo dữ liệu giả lập thông qua việc nhắc nhở thủ công bao gồm việc tạo ra các tập dữ liệu nhân tạo bắt chước các kiểu sử dụng trong thế giới thực.
 ```
 
-The `response` is now more complex and specific to the `instruction`. This is a good start, but as we have seen with EvolInstruct, evolved generations are not always better. Hence, it is important to use additional evaluation techniques to ensure the quality of the dataset. We will explore this in the next section.
+`Phản hồi` bây giờ phức tạp hơn và cụ thể hơn cho `hướng dẫn`. Đây là một khởi đầu tốt, nhưng như chúng ta đã thấy với EvolInstruct, các thế hệ tiến hóa không phải lúc nào cũng tốt hơn. Do đó, điều quan trọng là phải sử dụng các kỹ thuật đánh giá bổ sung để đảm bảo chất lượng của tập dữ liệu. Chúng ta sẽ khám phá điều này trong phần tiếp theo.
 
-## Creating Scores
+## Tạo điểm số
 
-Scores are a measure of how much one response is preferred over another. In general, these scores can be absolute, subjective, or relative. For this course, we will focus on the first two because they are most valuable for creating preference datasets. This scoring is a way of judging and evaluating using language models and therefore has some overlap with the evaluation techniques we have seen in [the chapter on evaluation](../3_evaluation/README.md). As with the other evaluation techniques, scores and evaluations normally require larger models to better align with human preferences.
+Điểm số là thước đo mức độ phản hồi này được ưa thích hơn phản hồi khác. Nhìn chung, những điểm số này có thể là tuyệt đối, chủ quan hoặc tương đối. Đối với khóa học này, chúng ta sẽ tập trung vào hai loại đầu tiên vì chúng có giá trị nhất để tạo các tập dữ liệu ưu tiên. Việc chấm điểm này là một cách đánh giá và nhận xét bằng cách sử dụng các mô hình ngôn ngữ và do đó có một số điểm tương đồng với các kỹ thuật đánh giá mà chúng ta đã thấy trong [chương về đánh giá](../3_evaluation/README.md). Cũng như các kỹ thuật đánh giá khác, điểm số và đánh giá thường yêu cầu các mô hình lớn hơn để phù hợp hơn với ưu tiên của con người.
 
 ### UltraFeedback
 
-UltraFeedback is a technique that generates scores and critiques for a given `prompt` and its `completion`.
+UltraFeedback là một kỹ thuật tạo ra điểm số và phê bình cho một `lời nhắc` nhất định và `phần hoàn thành` của nó.
 
-The scores are based on the quality of the `completion` according to a set of criteria. There are four fine-grained criteria: `helpfulness`, `relevance`, `deepening`, and `creativity`. These are useful but generally speaking, using the overall criteria is a good start, which allows us to simplify the process of generating scores. The scores can be used to determine which `completion` is the `chosen` and which is the `rejected` one. Because they are absolute, they can also be used as interesting filters for outliers in the dataset, either finding the worst completions or the pairs with more or less difference.
+Điểm số dựa trên chất lượng của `phần hoàn thành` theo một tập hợp các tiêu chí. Có bốn tiêu chí chi tiết: `helpfulness` (tính hữu ích), `relevance` (mức độ liên quan), `deepening` (đào sâu) và `creativity` (sáng tạo). Chúng rất hữu ích nhưng nói chung, sử dụng các tiêu chí tổng thể là một khởi đầu tốt, cho phép chúng ta đơn giản hóa quá trình tạo điểm số. Điểm số có thể được sử dụng để xác định `phần hoàn thành` nào là `chosen` và phần nào là `rejected`. Bởi vì chúng là tuyệt đối, chúng cũng có thể được sử dụng làm bộ lọc thú vị cho các giá trị ngoại lệ trong tập dữ liệu, tìm các phần hoàn thành tệ nhất hoặc các cặp có ít nhiều sự khác biệt.
 
-The critiques are added to provide reasoning for the score. They can be used as extra context to help us understand the differences between the scores. The language model generates extensive critiques which is very useful, but this also introduces extra cost and complexity to the process because generating critiques is more expensive than generating a single token to represent a score.
+Các phê bình được thêm vào để cung cấp lý do cho điểm số. Chúng có thể được sử dụng làm ngữ cảnh bổ sung để giúp chúng ta hiểu sự khác biệt giữa các điểm số. Mô hình ngôn ngữ tạo ra các phê bình sâu rộng rất hữu ích, nhưng điều này cũng làm tăng thêm chi phí và độ phức tạp cho quá trình vì việc tạo ra các phê bình tốn kém hơn so với việc tạo ra một token duy nhất để đại diện cho điểm số.
 
-The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback) and a simplified version is shown below:
+Lời nhắc được [triển khai trong distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback) và phiên bản đơn giản hóa được hiển thị bên dưới:
 
 ```bash
-Evaluate the model's outputs based on various criteria: Helpfulness, Relevance, Deepening, Creativity
-Your role is to provide a holistic assessment based on the above factors.
-Score the output from 1 to 5 on overall quality.
+Đánh giá đầu ra của mô hình dựa trên các tiêu chí khác nhau: Tính hữu ích, Mức độ liên quan, Đào sâu, Sáng tạo
+Vai trò của bạn là cung cấp một đánh giá tổng thể dựa trên các yếu tố trên.
+Chấm điểm đầu ra từ 1 đến 5 về chất lượng tổng thể.
 
-Answer with the following format: score - rationale
+Trả lời theo định dạng sau: điểm số - lý do
 
-# Input
+# Đầu vào
 {{ input }}
 
-# Response
+# Phản hồi
 {{ output }}
 
-# Score - Rationale
+# Điểm số - Lý do
 ```
 
-Let's use the [UltraFeedback class](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) to evaluate the synthetic `prompt` and `completion` from [the Model Pooling section](#model-pooling).
+Hãy sử dụng [lớp UltraFeedback](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) để đánh giá `lời nhắc` và `phần hoàn thành` tổng hợp từ [phần Tổng hợp mô hình](#model-pooling).
 
 ```python
 from distilabel.llms import TransformersLLM
@@ -121,9 +121,9 @@ llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
 ultrafeedback = UltraFeedback(llm=llm)
 ultrafeedback.load()
 
-instruction = "What is synthetic data?"
-completion_a = "Synthetic data is artificially generated data that mimics real-world usage."
-completion_b = "Synthetic data refers to data that has been generated artificially."
+instruction = "Dữ liệu giả lập (synthetic data) là gì?"
+completion_a = "Dữ liệu giả lập là dữ liệu được tạo ra nhân tạo, bắt chước cách sử dụng trong thế giới thực."
+completion_b = "Dữ liệu giả lập đề cập đến dữ liệu đã được tạo ra một cách nhân tạo."
 
 next(ultrafeedback.process([{
     "instruction": instruction,
@@ -132,24 +132,24 @@ next(ultrafeedback.process([{
 # [
 #     {
 #         'ratings': [4, 5],
-#         'rationales': ['could have been more specific', 'good definition'],
+#         'rationales': ['có thể cụ thể hơn', 'định nghĩa tốt'],
 #     }
 # ]
 ```
 
-## Best Practices
+## Các phương pháp hay nhất
 
-- Overall scores are cheaper and easier to generate than critiques and specific scores
-- Use bigger models to generate scores and critiques
-- Use a diverse set of models to generate scores and critiques
-- Iterate on configuration of the `system_prompt` and models
+- Phương pháp đánh giá điểm số tổng thể thường rẻ hơn và dễ tạo hơn so với phê bình và điểm số cụ thể
+- Sử dụng các mô hình lớn hơn để tạo điểm số và phê bình
+- Sử dụng một tập hợp đa dạng các mô hình để tạo điểm số và phê bình
+- Lặp lại cấu hình của `system_prompt` và các mô hình
 
-## Next Steps
+## Các bước tiếp theo
 
-👨🏽‍💻 Code -[Exercise Notebook](./notebooks/preference_dpo_dataset.ipynb) to generate a dataset for instruction tuning
+👨🏽‍💻 Lập trình -[Notebook bài tập](./notebooks/preference_dpo_dataset.ipynb) để tạo tập dữ liệu để tinh chỉnh hướng dẫn
 
-## References
+## Tài liệu tham khảo
 
-- [Distilabel Documentation](https://distilabel.argilla.io/latest/)
+- [Tài liệu Distilabel](https://distilabel.argilla.io/latest/)
 - [Deita](https://arxiv.org/abs/2312.15685)
 - [UltraFeedback](https://arxiv.org/abs/2310.01377)

From eece21a7cdba75c15ebdd9f1ae5431b939699098 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 22 Jan 2025 21:53:39 +0100
Subject: [PATCH 59/63] init

---
 .../notebooks/instruction_sft_dataset.ipynb   | 53 ++++++++++---------
 1 file changed, 27 insertions(+), 26 deletions(-)

diff --git a/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
index 54584722..ef13b3e9 100644
--- a/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
+++ b/vi/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb
@@ -4,20 +4,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Generate a dataset for instruction tuning\n",
+    "# Tạo tập dữ liệu hướng dẫn (instruction tuning)\n",
     "\n",
-    "This notebook will guide you through the process of generating a dataset for instruction tuning. We'll use the `distilabel` package to generate a dataset for instruction tuning.\n",
+    "Notebook này sẽ hướng dẫn bạn qua quá trình tạo tập dữ liệu để tinh chỉnh hướng dẫn. Chúng ta sẽ sử dụng package `distilabel` để tạo tập dữ liệu cho việc tinh chỉnh hướng dẫn.\n",
     "\n",
-    "So let's dig in to some instruction tuning datasets.\n",
+    "Vì vậy, hãy cùng tìm hiểu sâu hơn về một số tập dữ liệu tinh chỉnh hướng dẫn.\n",
     "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Generate a dataset for instruction tuning</h2>\n",
-    "    <p>Now that you've seen how to generate a dataset for instruction tuning, try generating a dataset for instruction tuning.</p>\n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Generate an instruction tuning dataset</p>\n",
-    "    <p>🐕 Generate a dataset for instruction tuning with seed data</p>\n",
-    "    <p>🦁 Generate a dataset for instruction tuning with seed data and with instruction evolution</p>\n",
-    "</div>"
+    " <div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "     <h2 style='margin: 0;color:blue'>Bài tập: Tạo tập dữ liệu hướng dẫn</h2>\n",
+    "     <p>Bây giờ bạn đã biết cách tạo tập dữ liệu để tinh chỉnh hướng dẫn, hãy thử tạo tập dữ liệu để tinh chỉnh hướng dẫn.</p>\n",
+    "     <p><b>Các mức độ khó</b></p>\n",
+    "     <p>🐢 Tạo tập dữ liệu tinh chỉnh hướng dẫn</p>\n",
+    "     <p>🐕 Tạo tập dữ liệu để tinh chỉnh hướng dẫn với dữ liệu hạt giống (seed data)</p>\n",
+    "     <p>🦁 Tạo tập dữ liệu để tinh chỉnh hướng dẫn với dữ liệu hạt giống và với sự tiến hóa hướng dẫn (instruction evolution)</p>\n",
+    " </div>"
    ]
   },
   {
@@ -28,9 +28,9 @@
     }
    },
    "source": [
-    "## Install dependencies\n",
+    "## Cài đặt thư viện\n",
     "\n",
-    "Instead of transformers, you can also install `vllm` or `hf-inference-endpoints`."
+    "Thay vì `transformers`, bạn cũng có thể cài đặt `vllm` hoặc `hf-inference-endpoints`."
    ]
   },
   {
@@ -46,13 +46,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Start synthesizing\n",
+    "## Bắt đầu tạo dữ liệu\n",
     "\n",
-    "As we've seen in the previous course content, we can create a distilabel pipelines for instruction dataset generation. The bare minimum pipline is already provided. Make sure to scale up this pipeline to generate a large dataset for instruction tuning. Swap out models, model providers and generation arguments to see how they affect the quality of the dataset. Experiment small, scale up later.\n",
+    "Như chúng ta đã thấy trong nội dung khóa học trước, chúng ta có thể tạo các quy trình (pipeline) `distilabel` để tạo tập dữ liệu hướng dẫn. Quy trình tối thiểu đã được cung cấp. Đảm bảo mở rộng quy trình này để tạo tập dữ liệu lớn cho việc tinh chỉnh hướng dẫn. Thay đổi các mô hình và các đối số (generation arguments) để xem chúng ảnh hưởng như thế nào đến chất lượng của tập dữ liệu. Thử nghiệm nhỏ, mở rộng quy mô sau.\n",
     "\n",
-    "Check out the [distilabel components gallery](https://distilabel.argilla.io/latest/components-gallery/) for information about the processing classes and how to use them. \n",
+    "Hãy tham khảo [thư viện các thành phần distilabel](https://distilabel.argilla.io/latest/components-gallery/) để biết thông tin về các lớp xử lý và cách sử dụng chúng.\n",
     "\n",
-    "An example of loading data from the Hub instead of dictionaries is provided below.\n",
+    "Một ví dụ về việc tải dữ liệu từ Hub thay vì ở dạng thư viện (dictionary) được cung cấp bên dưới.\n",
     "\n",
     "```python\n",
     "from datasets import load_dataset\n",
@@ -65,7 +65,7 @@
     "    distiset = pipeline.run(dataset=dataset)\n",
     "```\n",
     "\n",
-    "Don't forget to push your dataset to the Hub after running the pipeline!"
+    "Đừng quên đẩy tập dữ liệu của bạn lên Hub sau khi chạy quy trình!"
    ]
   },
   {
@@ -80,7 +80,7 @@
     "from distilabel.steps.tasks import TextGeneration\n",
     "\n",
     "with Pipeline() as pipeline:\n",
-    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Generate a short question about the Hugging Face Smol-Course.\"}])\n",
+    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Tạo một câu hỏi ngắn về Khóa học Smol của Hugging Face.\"}])\n",
     "    llm = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
     "    gen_a = TextGeneration(llm=llm, output_mappings={\"generation\": \"instruction\"})\n",
     "    gen_b = TextGeneration(llm=llm, output_mappings={\"generation\": \"response\"})\n",
@@ -95,17 +95,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🌯 That's a wrap\n",
+    "## 🌯 Kết thúc\n",
     "\n",
-    "You've now seen how to generate a dataset for instruction tuning. You could use this to:\n",
+    "Bây giờ bạn đã biết cách tạo tập dữ liệu để tinh chỉnh hướng dẫn. Bạn có thể sử dụng điều này để:\n",
     "\n",
-    "- Generate a dataset for instruction tuning.\n",
-    "- Create evaluation datasets for instruction tuning.\n",
+    " - Tạo tập dữ liệu để tinh chỉnh hướng dẫn.\n",
+    " - Tạo tập dữ liệu đánh giá để tinh chỉnh hướng dẫn.\n",
     "\n",
-    "Next\n",
+    "Tiếp theo\n",
     "\n",
-    "🧑‍🏫 Learn - About [generating preference datasets](./preference_datasets.md)\n",
-    "🏋️‍♂️ Fine-tune a model for instruction tuning with a synthetic dataset based on the [instruction tuning chapter](../../1_instruction_tuning/README.md)\n"
+    "🧑‍🏫 Tìm hiểu - Về [tạo tập dữ liệu ưu tiên](./preference_datasets.md)\n",
+    "\n",
+    "🏋️‍♂️ Tinh chỉnh một mô hình để tinh chỉnh hướng dẫn với tập dữ liệu giả lập dựa trên [chương tinh chỉnh hướng dẫn](../../1_instruction_tuning/README.md)"
    ]
   }
  ],

From e83f139cb99ca8ac0761c69623e5ae0433241b11 Mon Sep 17 00:00:00 2001
From: hahuyhoang411 <hahuyhoanghhh41@gmail.com>
Date: Wed, 22 Jan 2025 22:07:26 +0100
Subject: [PATCH 60/63] init

---
 .../notebooks/preference_dpo_dataset.ipynb    | 58 +++++++++----------
 1 file changed, 29 insertions(+), 29 deletions(-)

diff --git a/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
index cd9747df..109226da 100644
--- a/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
+++ b/vi/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb
@@ -4,20 +4,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Generate a dataset for preference alignment\n",
+    "# Tạo tập dữ liệu để điều chỉnh ưu tiên (preference alignment)\n",
     "\n",
-    "This notebook will guide you through the process of generating a dataset for preference alignment. We'll use the `distilabel` package to generate a dataset for preference alignment.\n",
+    "Notebook này sẽ hướng dẫn bạn qua quá trình tạo tập dữ liệu để điều chỉnh ưu tiên. Chúng ta sẽ sử dụng thư viện `distilabel` để tạo tập dữ liệu cho việc điều chỉnh ưu tiên.\n",
     "\n",
-    "So let's dig in to some preference alignment datasets.\n",
+    "Vì vậy, hãy cùng tìm hiểu sâu hơn về một số tập dữ liệu điều chỉnh ưu tiên.\n",
     "\n",
-    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
-    "    <h2 style='margin: 0;color:blue'>Exercise: Generate a dataset for preference alignment</h2>\n",
-    "    <p>Now that you've seen how to generate a dataset for preference alignment, try generating a dataset for preference alignment.</p>\n",
-    "    <p><b>Difficulty Levels</b></p>\n",
-    "    <p>🐢 Generate a dataset for preference alignment</p>\n",
-    "    <p>🐕 Generate a dataset for preference alignment with response evolution</p>\n",
-    "    <p>🦁 Generate a dataset for preference alignment with response evolution and model pooling</p>\n",
-    "</div>"
+    " <div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
+    "     <h2 style='margin: 0;color:blue'>Bài tập: Tạo tập dữ liệu để điều chỉnh sở thích</h2>\n",
+    "     <p>Bây giờ bạn đã biết cách tạo tập dữ liệu để điều chỉnh sở thích, hãy thử tạo tập dữ liệu để điều chỉnh sở thích.</p>\n",
+    "     <p><b>Các mức độ khó</b></p>\n",
+    "     <p>🐢 Tạo tập dữ liệu để điều chỉnh sở thích</p>\n",
+    "     <p>🐕 Tạo tập dữ liệu để điều chỉnh sở thích với sự tiến hóa phản hồi (response evolution)</p>\n",
+    "     <p>🦁 Tạo tập dữ liệu để điều chỉnh sở thích với sự tiến hóa phản hồi và tổng hợp mô hình (model pooling)</p>\n",
+    " </div>"
    ]
   },
   {
@@ -28,9 +28,9 @@
     }
    },
    "source": [
-    "## Install dependencies\n",
+    "## Cài đặt các thư viện\n",
     "\n",
-    "Instead of transformers, you can also install `vllm` or `hf-inference-endpoints`."
+    "Thay vì `transformers`, bạn cũng có thể cài đặt `vllm` hoặc `hf-inference-endpoints`."
    ]
   },
   {
@@ -46,26 +46,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Start synthesizing\n",
+    "## Bắt đầu tổng hợp\n",
     "\n",
-    "As we've seen in the previous notebook, we can create a distilabel pipeline for preference dataset generation. The bare minimum pipline is already provided. You can continue work on this pipeline to generate a large dataset for preference alignment. Swap out models, model providers and generation arguments to see how they affect the quality of the dataset. Experiment small, scale up later.\n",
+    "Như chúng ta đã thấy trong notebook trước, chúng ta có thể tạo một quy trình (pipeline) distilabel để tạo tập dữ liệu sở thích. Quy trình tối thiểu đã được cung cấp. Bạn có thể tiếp tục làm việc trên quy trình này để tạo tập dữ liệu lớn cho việc điều chỉnh sở thích. Thay đổi các mô hình, nhà cung cấp mô hình và các đối số sinh (generation arguments) để xem chúng ảnh hưởng như thế nào đến chất lượng của tập dữ liệu. Thử nghiệm nhỏ, mở rộng quy mô sau.\n",
     "\n",
-    "Check out the [distilabel components gallery](https://distilabel.argilla.io/latest/components-gallery/) for information about the processing classes and how to use them. \n",
+    "Hãy xem [thư viện các thành phần distilabel](https://distilabel.argilla.io/latest/components-gallery/) để biết thông tin về các lớp xử lý và cách sử dụng chúng.\n",
     "\n",
-    "An example of loading data from the Hub instead of dictionaries is provided below.\n",
+    "Một ví dụ về việc tải dữ liệu từ Hub thay vì các dictionary được cung cấp bên dưới.\n",
     "\n",
     "```python\n",
     "from datasets import load_dataset\n",
     "\n",
     "with Pipeline(...) as pipeline:\n",
-    "    ...\n",
+    "     ...\n",
     "\n",
-    "if __name__ == \"__main__:\n",
-    "    dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
-    "    distiset = pipeline.run(dataset=dataset)\n",
+    "if __name__ == \"__main__\":\n",
+    "     dataset = load_dataset(\"my-dataset\", split=\"train\")\n",
+    "     distiset = pipeline.run(dataset=dataset)\n",
     "```\n",
     "\n",
-    "Don't forget to push your dataset to the Hub after running the pipeline!"
+    "Đừng quên đẩy tập dữ liệu của bạn lên Hub sau khi chạy quy trình!"
    ]
   },
   {
@@ -80,7 +80,7 @@
     "from distilabel.steps.tasks import TextGeneration\n",
     "\n",
     "with Pipeline() as pipeline:\n",
-    "    data = LoadDataFromDicts(data=[{\"instruction\": \"What is synthetic data?\"}])\n",
+    "    data = LoadDataFromDicts(data=[{\"instruction\": \"Dữ liệu giả lập (synthetic data) là gì?\"}])\n",
     "    llm_a = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n",
     "    gen_a = TextGeneration(llm=llm_a)\n",
     "    llm_b = TransformersLLM(model=\"Qwen/Qwen2.5-1.5B-Instruct\")\n",
@@ -97,16 +97,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🌯 That's a wrap\n",
+    "## 🌯 Kết thúc\n",
     "\n",
-    "You've now seen how to generate a dataset for preference alignment. You could use this to:\n",
+    "Bây giờ bạn đã biết cách tạo tập dữ liệu để điều chỉnh ưu tiên. Bạn có thể sử dụng điều này để:\n",
     "\n",
-    "- Generate a dataset for preference alignment.\n",
-    "- Create evaluation datasets for preference alignment.\n",
+    "- Tạo tập dữ liệu để điều chỉnh ưu tiên.\n",
+    "- Tạo tập dữ liệu đánh giá để điều chỉnh ưu tiên.\n",
     "\n",
-    "Next\n",
+    "Tiếp theo\n",
     "\n",
-    "🏋️‍♂️ Fine-tune a model with preference alignment with a synthetic dataset based on the [preference tuning chapter](../../2_preference_alignment/README.md) \n"
+    "🏋️‍♂️ Tinh chỉnh một mô hình với điều chỉnh ưu tiên với tập dữ liệu giả lập dựa trên [chương tinh chỉnh ưu tiên](../../2_preference_alignment/README.md)"
    ]
   }
  ],

From 60c2a62ac04690875b5b158ea6372475e28d8bef Mon Sep 17 00:00:00 2001
From: jeetundaviya <jeet.undaviya20017@gmail.com>
Date: Sat, 29 Mar 2025 23:25:22 +0530
Subject: [PATCH 61/63] Completed exercise for chat_templates_example

---
 .../notebooks/chat_templates_example.ipynb    | 285 ++++++++++++++++--
 1 file changed, 258 insertions(+), 27 deletions(-)

diff --git a/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/1_instruction_tuning/notebooks/chat_templates_example.ipynb
index 93772206..361366e8 100644
--- a/1_instruction_tuning/notebooks/chat_templates_example.ipynb
+++ b/1_instruction_tuning/notebooks/chat_templates_example.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -21,15 +21,25 @@
     "id": "K-lZu8JvtwUN",
     "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
+     ]
+    }
+   ],
    "source": [
     "# Install the requirements in Google Colab\n",
     "# !pip install transformers datasets trl huggingface_hub\n",
     "\n",
     "# Authenticate to Hugging Face\n",
     "from huggingface_hub import login\n",
-    "\n",
-    "login()\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "load_dotenv('../.env')\n",
+    "login(token=os.environ.get('HF_TOKEN'))\n",
     "\n",
     "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
    ]
@@ -160,7 +170,120 @@
     "id": "Nrxh0oX6tFls",
     "outputId": "953e1527-8168-4346-9338-6e188ca31a1a"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f4d60bc9e374c58ac8527197fe75fcb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/704 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5218527784f343f1905fd3ff5d71b319",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/269M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f6913b6e3ea43a69fe99223ae0af2f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a485bca70d774257a1a799902b473012",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/3.66k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e14b4e6584194989807d5b1079ed8d2f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/801k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11a7301c9bcc4e149031567b03f5011e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "153abae184e0440eaa33830038f99b64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/2.10M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f2d1aaae52d4baaaa06ca9b20c6b736",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/831 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Dynamically set the device\n",
     "device = (\n",
@@ -248,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -332,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -351,9 +474,6 @@
        "  width=\"100%\"\n",
        "  height=\"360px\"\n",
        "></iframe>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
@@ -361,9 +481,8 @@
     }
    ],
    "source": [
-    "from IPython.core.display import display, HTML\n",
-    "\n",
-    "display(\n",
+    "from IPython.core.display import  HTML,display_html\n",
+    "display_html(\n",
     "    HTML(\n",
     "        \"\"\"<iframe\n",
     "  src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n",
@@ -378,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -466,7 +585,36 @@
     "id": "4p3atw4_tFlu",
     "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6993eb2f13204319ab97578a3d135d64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/2260 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d69ef5216cb145eb80c5c73ad2864d82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/119 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from datasets import load_dataset\n",
     "\n",
@@ -476,6 +624,8 @@
     "def process_dataset(sample):\n",
     "    # TODO: 🐢 Convert the sample into a chat format\n",
     "    # use the tokenizer's method to apply the chat template\n",
+    "    \n",
+    "    sample['messages'] = tokenizer.apply_chat_template(conversation=sample['messages'],add_generation_prompt=False,tokenize=False)\n",
     "    return sample\n",
     "\n",
     "\n",
@@ -484,7 +634,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<|im_start|>user\n",
+      "Hi there<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "Hello! How can I help you today?<|im_end|>\n",
+      "<|im_start|>user\n",
+      "I'm looking for a beach resort for my next vacation. Can you recommend some popular ones?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "Some popular beach resorts include Maui in Hawaii, the Maldives, and the Bahamas. They're known for their beautiful beaches and crystal-clear waters.<|im_end|>\n",
+      "<|im_start|>user\n",
+      "That sounds great. Are there any resorts in the Caribbean that are good for families?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "Yes, the Turks and Caicos Islands and Barbados are excellent choices for family-friendly resorts in the Caribbean. They offer a range of activities and amenities suitable for all ages.<|im_end|>\n",
+      "<|im_start|>user\n",
+      "Okay, I'll look into those. Thanks for the recommendations!<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "You're welcome. I hope you find the perfect resort for your vacation.<|im_end|>\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ds['train']['messages'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -503,9 +686,6 @@
        "  width=\"100%\"\n",
        "  height=\"360px\"\n",
        "></iframe>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
@@ -513,7 +693,7 @@
     }
    ],
    "source": [
-    "display(\n",
+    "display_html(\n",
     "    HTML(\n",
     "        \"\"\"<iframe\n",
     "  src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n",
@@ -528,29 +708,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {
     "collapsed": true,
     "id": "bWUSv7NMtFlu"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8536572bc1154324ba82be1fb545cc5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/7473 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a94119cb8bc4496adbe4e004085527f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1319 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ds = load_dataset(\"openai/gsm8k\", \"main\")\n",
     "\n",
     "\n",
     "def process_dataset(sample):\n",
     "    # TODO: 🐕 Convert the sample into a chat format\n",
-    "\n",
+    "    qs,ans = sample['question'],sample['answer']\n",
     "    # 1. create a message format with the role and content\n",
-    "\n",
+    "    conversation = [{\"role\":\"user\",\"content\":qs},{\"role\":\"assistant\",\"content\":ans}]\n",
     "    # 2. apply the chat template to the samples using the tokenizer's method\n",
-    "\n",
+    "    sample['messages'] = tokenizer.apply_chat_template(conversation=conversation,tokenize=False,add_generation_prompt=False)\n",
     "    return sample\n",
     "\n",
     "\n",
     "ds = ds.map(process_dataset)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?',\n",
+       " 'answer': 'Natalia sold 48/2 = <<48/2=24>>24 clips in May.\\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\\n#### 72',\n",
+       " 'messages': '<|im_start|>user\\nNatalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?<|im_end|>\\n<|im_start|>assistant\\nNatalia sold 48/2 = <<48/2=24>>24 clips in May.\\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\\n#### 72<|im_end|>\\n'}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds['train'][0]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -570,7 +801,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "py310",
+   "display_name": "env_for_llama_cpp",
    "language": "python",
    "name": "python3"
   },
@@ -584,7 +815,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.11.11"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

From 72318c8924702866d66fbb4f88143ccbf79a4807 Mon Sep 17 00:00:00 2001
From: jeetundaviya <jeet.undaviya20017@gmail.com>
Date: Sun, 30 Mar 2025 04:08:20 +0530
Subject: [PATCH 62/63] Finished Exercise in sft_finetuning_example.ipynb

---
 .../notebooks/sft_finetuning_example.ipynb    | 310 ++++++++++++++++--
 1 file changed, 285 insertions(+), 25 deletions(-)

diff --git a/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
index d18479a9..8b8990fb 100644
--- a/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
+++ b/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb
@@ -20,9 +20,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
+     ]
+    }
+   ],
    "source": [
     "# Install the requirements in Google Colab\n",
     "# !pip install transformers datasets trl huggingface_hub\n",
@@ -30,14 +38,18 @@
     "# Authenticate to Hugging Face\n",
     "\n",
     "from huggingface_hub import login\n",
-    "login()\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "load_dotenv('../.env')\n",
+    "login(token=os.environ.get('HF_TOKEN'))\n",
+    "os.environ['PYTORCH_MPS_HIGH_WATERMARK_RATIO'] = \"0.0\"\n",
     "\n",
     "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,9 +91,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before training:\n",
+      "user\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a haiku about programming\n",
+      "Write a\n"
+     ]
+    }
+   ],
    "source": [
     "# Let's test the base model before training\n",
     "prompt = \"Write a haiku about programming\"\n",
@@ -110,9 +147,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    }
+   ],
    "source": [
     "# Load a sample dataset\n",
     "from datasets import load_dataset\n",
@@ -123,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,25 +197,33 @@
     "sft_config = SFTConfig(\n",
     "    output_dir=\"./sft_output\",\n",
     "    max_steps=1000,  # Adjust based on dataset size and desired training duration\n",
-    "    per_device_train_batch_size=4,  # Set according to your GPU memory capacity\n",
+    "    per_device_train_batch_size=2,  # Set according to your GPU memory capacity\n",
     "    learning_rate=5e-5,  # Common starting point for fine-tuning\n",
     "    logging_steps=10,  # Frequency of logging training metrics\n",
     "    save_steps=100,  # Frequency of saving model checkpoints\n",
-    "    evaluation_strategy=\"steps\",  # Evaluate the model at regular intervals\n",
+    "    eval_strategy=\"steps\",  # Evaluate the model at regular intervals\n",
     "    eval_steps=50,  # Frequency of evaluation\n",
-    "    use_mps_device=(\n",
-    "        True if device == \"mps\" else False\n",
-    "    ),  # Use MPS for mixed precision training\n",
     "    hub_model_id=finetune_name,  # Set a unique name for your model\n",
+    "    dataset_text_field='text',\n",
+    "    # eval_packing=False,\n",
+    "    # packing=True\n",
     ")\n",
     "\n",
+    "def process_dataset(sample):\n",
+    "    print(sample)\n",
+    "    print(tokenizer.chat_template)\n",
+    "    sample['text'] = tokenizer.apply_chat_template(sample['messages'],tokenize=False,add_generation_prompt=True)\n",
+    "    print(sample)\n",
+    "    return sample\n",
+    "\n",
     "# Initialize the SFTTrainer\n",
     "trainer = SFTTrainer(\n",
     "    model=model,\n",
     "    args=sft_config,\n",
     "    train_dataset=ds[\"train\"],\n",
-    "    tokenizer=tokenizer,\n",
     "    eval_dataset=ds[\"test\"],\n",
+    "    # formatting_func=process_dataset,\n",
+    "processing_class=tokenizer\n",
     ")\n",
     "\n",
     "# TODO: 🦁 🐕 align the SFTTrainer params with your chosen dataset. For example, if you are using the `bigcode/the-stack-smol` dataset, you will need to choose the `content` column`"
@@ -184,10 +240,140 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [1000/1000 10:02, Epoch 0/1]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>50</td>\n",
+       "      <td>1.184200</td>\n",
+       "      <td>1.186506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>100</td>\n",
+       "      <td>1.088200</td>\n",
+       "      <td>1.143443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>150</td>\n",
+       "      <td>1.113400</td>\n",
+       "      <td>1.131009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>200</td>\n",
+       "      <td>1.150600</td>\n",
+       "      <td>1.118932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>250</td>\n",
+       "      <td>1.059700</td>\n",
+       "      <td>1.104667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>300</td>\n",
+       "      <td>1.070100</td>\n",
+       "      <td>1.093120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>350</td>\n",
+       "      <td>1.035800</td>\n",
+       "      <td>1.084094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>400</td>\n",
+       "      <td>1.034100</td>\n",
+       "      <td>1.077743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>450</td>\n",
+       "      <td>1.044900</td>\n",
+       "      <td>1.073178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>500</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.067063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>550</td>\n",
+       "      <td>1.107800</td>\n",
+       "      <td>1.059354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>600</td>\n",
+       "      <td>1.029100</td>\n",
+       "      <td>1.056215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>650</td>\n",
+       "      <td>1.037100</td>\n",
+       "      <td>1.051449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>700</td>\n",
+       "      <td>0.989800</td>\n",
+       "      <td>1.049918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>750</td>\n",
+       "      <td>1.025200</td>\n",
+       "      <td>1.046517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>800</td>\n",
+       "      <td>1.008200</td>\n",
+       "      <td>1.045583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>850</td>\n",
+       "      <td>1.055300</td>\n",
+       "      <td>1.041860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>900</td>\n",
+       "      <td>1.019400</td>\n",
+       "      <td>1.039372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>950</td>\n",
+       "      <td>0.970600</td>\n",
+       "      <td>1.038175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1000</td>\n",
+       "      <td>1.110400</td>\n",
+       "      <td>1.037109</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "\n",
     "# Train the model\n",
     "trainer.train()\n",
     "\n",
@@ -197,9 +383,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b3370bcb19a45f9b3e53e9793d58432",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/538M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ed653609ab946fbbd5afd96524de294",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "training_args.bin:   0%|          | 0.00/5.62k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "002ecc31c93b490a9a9a8477a0cc35ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/jeetundaviya/SmolLM2-FT-MyDataset/commit/ce5e7a7aa1766fd7773f5edb026fd6b7b751fee1', commit_message='End of training', commit_description='', oid='ce5e7a7aa1766fd7773f5edb026fd6b7b751fee1', pr_url=None, repo_url=RepoUrl('https://huggingface.co/jeetundaviya/SmolLM2-FT-MyDataset', endpoint='https://huggingface.co', repo_type='model', repo_id='jeetundaviya/SmolLM2-FT-MyDataset'), pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "trainer.push_to_hub(tags=finetune_tags)"
    ]
@@ -216,9 +455,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After training:\n",
+      "user\n",
+      "Write a haiku about programming\n",
+      "assistant\n",
+      "Hello! How can I help you today? I'm going to write a haiku about programming. What is programming? It's a way of writing and understanding computer programs. Can you tell me more about it?\n",
+      "alas\n",
+      "What is programming? It's a way of writing and understanding computer programs. What's a computer program? It's a set of instructions that tells a computer what to do. Can you give me an example of a computer program?\n",
+      "alas\n",
+      "A\n"
+     ]
+    }
+   ],
    "source": [
     "# Test the fine-tuned model on the same prompt\n",
     "\n",
@@ -232,7 +487,12 @@
     "# Generate response\n",
     "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
     "\n",
-    "# TODO: use the fine-tuned to model generate a response, just like with the base example."
+    "# TODO: use the fine-tuned to model generate a response, just like with the base example.\n",
+    "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n",
+    "new_model = AutoModelForCausalLM.from_pretrained('./SmolLM2-FT-MyDataset').to(device)\n",
+    "outputs = new_model.generate(**inputs, max_new_tokens=100)\n",
+    "print(\"After training:\")\n",
+    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
    ]
   },
   {
@@ -251,7 +511,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "py310",
+   "display_name": "env_for_llama_cpp",
    "language": "python",
    "name": "python3"
   },
@@ -265,7 +525,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

From 3ed18946618daa82811454dd37739de8670b3e17 Mon Sep 17 00:00:00 2001
From: jeetundaviya <jeet.undaviya20017@gmail.com>
Date: Sun, 30 Mar 2025 16:38:11 +0530
Subject: [PATCH 63/63] dpo half cooked training

---
 .../notebooks/dpo_finetuning_example.ipynb    | 176 ++++++++++++++++--
 1 file changed, 160 insertions(+), 16 deletions(-)

diff --git a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
index 3ddef6a6..23194e22 100644
--- a/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
+++ b/2_preference_alignment/notebooks/dpo_finetuning_example.ipynb
@@ -21,9 +21,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
+     ]
+    }
+   ],
    "source": [
     "# Install the requirements in Google Colab\n",
     "# !pip install transformers datasets trl huggingface_hub\n",
@@ -32,7 +40,10 @@
     "\n",
     "from huggingface_hub import login\n",
     "\n",
-    "login()\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "load_dotenv('../.env')\n",
+    "login(token=os.environ.get('HF_TOKEN'))\n",
     "\n",
     "# for convenience you can create an environment variable containing your hub token as HF_TOKEN"
    ]
@@ -46,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -74,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "id": "MCD77GZ60DOT"
    },
@@ -88,11 +99,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['chosen', 'rejected', 'score_chosen', 'score_rejected'],\n",
+       "    num_rows: 62135\n",
+       "})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# TODO: 🐕 If your dataset is not represented as conversation lists, you can use the `process_dataset` function to convert it."
+    "# TODO: 🐕 If your dataset is not represented as conversation lists, you can use the `process_dataset` function to convert it.\n",
+    "dataset"
    ]
   },
   {
@@ -111,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -271,7 +297,7 @@
     "    # Keep all columns in dataset even if not used\n",
     "    remove_unused_columns=False,\n",
     "    # Enable MPS (Metal Performance Shaders) for Mac devices\n",
-    "    use_mps_device=device == \"mps\",\n",
+    "    # use_mps_device=device == \"mps\",\n",
     "    # Model ID for HuggingFace Hub uploads\n",
     "    hub_model_id=finetune_name,\n",
     "    # DPO-specific temperature parameter that controls the strength of the preference model\n",
@@ -286,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,9 +337,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='20' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [ 20/200 37:35 < 6:15:59, 0.01 it/s, Epoch 0.00/1]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.693100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.693100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.692900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>0.693100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>0.692200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>0.691700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>0.689800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>0.692100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>0.689400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>0.690600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>0.686500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>0.686100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>0.687900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>0.683100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>0.686100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>0.689700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>0.694000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>0.670100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m \u001b[38;5;66;03m# Save the model\u001b[39;00m\n\u001b[32m      5\u001b[39m trainer.save_model(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m./\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfinetune_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.11/envs/env_for_llama_cpp/lib/python3.11/site-packages/transformers/trainer.py:2245\u001b[39m, in \u001b[36mTrainer.train\u001b[39m\u001b[34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[39m\n\u001b[32m   2243\u001b[39m         hf_hub_utils.enable_progress_bars()\n\u001b[32m   2244\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2245\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   2246\u001b[39m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[43m=\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2247\u001b[39m \u001b[43m        \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2248\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2249\u001b[39m \u001b[43m        \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m=\u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   2250\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.11/envs/env_for_llama_cpp/lib/python3.11/site-packages/transformers/trainer.py:2556\u001b[39m, in \u001b[36mTrainer._inner_training_loop\u001b[39m\u001b[34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[39m\n\u001b[32m   2549\u001b[39m context = (\n\u001b[32m   2550\u001b[39m     functools.partial(\u001b[38;5;28mself\u001b[39m.accelerator.no_sync, model=model)\n\u001b[32m   2551\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m i != \u001b[38;5;28mlen\u001b[39m(batch_samples) - \u001b[32m1\u001b[39m\n\u001b[32m   2552\u001b[39m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.accelerator.distributed_type != DistributedType.DEEPSPEED\n\u001b[32m   2553\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m contextlib.nullcontext\n\u001b[32m   2554\u001b[39m )\n\u001b[32m   2555\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m context():\n\u001b[32m-> \u001b[39m\u001b[32m2556\u001b[39m     tr_loss_step = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_items_in_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2558\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m   2559\u001b[39m     args.logging_nan_inf_filter\n\u001b[32m   2560\u001b[39m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[32m   2561\u001b[39m     \u001b[38;5;129;01mand\u001b[39;00m (torch.isnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch.isinf(tr_loss_step))\n\u001b[32m   2562\u001b[39m ):\n\u001b[32m   2563\u001b[39m     \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[32m   2564\u001b[39m     tr_loss = tr_loss + tr_loss / (\u001b[32m1\u001b[39m + \u001b[38;5;28mself\u001b[39m.state.global_step - \u001b[38;5;28mself\u001b[39m._globalstep_last_logged)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.11/envs/env_for_llama_cpp/lib/python3.11/site-packages/transformers/trainer.py:3764\u001b[39m, in \u001b[36mTrainer.training_step\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m   3761\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.accelerator.distributed_type == DistributedType.DEEPSPEED:\n\u001b[32m   3762\u001b[39m     kwargs[\u001b[33m\"\u001b[39m\u001b[33mscale_wrt_gas\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3764\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43maccelerator\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3766\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m loss.detach()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.11/envs/env_for_llama_cpp/lib/python3.11/site-packages/accelerate/accelerator.py:2359\u001b[39m, in \u001b[36mAccelerator.backward\u001b[39m\u001b[34m(self, loss, **kwargs)\u001b[39m\n\u001b[32m   2357\u001b[39m     \u001b[38;5;28mself\u001b[39m.lomo_backward(loss, learning_rate)\n\u001b[32m   2358\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2359\u001b[39m     \u001b[43mloss\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.11/envs/env_for_llama_cpp/lib/python3.11/site-packages/torch/_tensor.py:626\u001b[39m, in \u001b[36mTensor.backward\u001b[39m\u001b[34m(self, gradient, retain_graph, create_graph, inputs)\u001b[39m\n\u001b[32m    616\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m    617\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[32m    618\u001b[39m         Tensor.backward,\n\u001b[32m    619\u001b[39m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[32m   (...)\u001b[39m\u001b[32m    624\u001b[39m         inputs=inputs,\n\u001b[32m    625\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m626\u001b[39m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mautograd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    627\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43minputs\u001b[49m\n\u001b[32m    628\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.11/envs/env_for_llama_cpp/lib/python3.11/site-packages/torch/autograd/__init__.py:347\u001b[39m, in \u001b[36mbackward\u001b[39m\u001b[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[39m\n\u001b[32m    342\u001b[39m     retain_graph = create_graph\n\u001b[32m    344\u001b[39m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[32m    345\u001b[39m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[32m    346\u001b[39m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m347\u001b[39m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    348\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    349\u001b[39m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    350\u001b[39m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    351\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    352\u001b[39m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    353\u001b[39m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    354\u001b[39m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    355\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.11/envs/env_for_llama_cpp/lib/python3.11/site-packages/torch/autograd/graph.py:823\u001b[39m, in \u001b[36m_engine_run_backward\u001b[39m\u001b[34m(t_outputs, *args, **kwargs)\u001b[39m\n\u001b[32m    821\u001b[39m     unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[32m    822\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m823\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_execution_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[32m    824\u001b[39m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m    825\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[32m    826\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    827\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
+     ]
+    }
+   ],
    "source": [
     "# Train the model\n",
     "trainer.train()\n",
@@ -348,7 +492,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "env_for_llama_cpp",
    "language": "python",
    "name": "python3"
   },
@@ -362,7 +506,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.11.11"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {