From 48600a8b2633c004b5062d8c5c240e05d48306f3 Mon Sep 17 00:00:00 2001
From: YeonwooSung <neos960518@gmail.com>
Date: Sun, 29 Sep 2024 16:57:51 +0900
Subject: [PATCH] fix: Fix up constants

---
 .../Quantization_of_Llama_2_with_GPTQ.ipynb   | 7357 ++++++++---------
 ...Llama_2_Chat_Models_on_Your_Computer.ipynb | 1806 ++--
 2 files changed, 4581 insertions(+), 4582 deletions(-)

diff --git a/LLMs/models/llama2/Quantization_of_Llama_2_with_GPTQ.ipynb b/LLMs/models/llama2/Quantization_of_Llama_2_with_GPTQ.ipynb
index da50803..b4d4969 100644
--- a/LLMs/models/llama2/Quantization_of_Llama_2_with_GPTQ.ipynb
+++ b/LLMs/models/llama2/Quantization_of_Llama_2_with_GPTQ.ipynb
@@ -1,1327 +1,886 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "machine_shape": "hm",
-      "gpuType": "T4"
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pX0IffauAEmI"
+      },
+      "source": [
+        "This notebook shows how to quantize Llama 2 with GPTQ.\n",
+        "It runs on Google Colab Pro. It can also run on a machine with at least 24 GB of CPU RAM and a GPU with 12 GB VRAM\n",
+        "\n",
+        "For more details check out this article:\n",
+        "[Quantization of Llama 2 with GTPQ for Fast Inference on Your Computer](https://kaitchup.substack.com/p/quantization-of-llama-2-with-gtpq)"
+      ]
     },
-    "language_info": {
-      "name": "python"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LlokkEwtAWEs"
+      },
+      "source": [
+        "We need the last version of AutoGPTQ, so we will install it from GitHub."
+      ]
     },
-    "accelerator": "GPU",
-    "widgets": {
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-            "_view_module_version": "1.5.0",
-            "_view_name": "HBoxView",
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-              "IPY_MODEL_eea87a848431477587a79db561f3013f",
-              "IPY_MODEL_c52bdd43a472471486bbbbcb96ee1a39",
-              "IPY_MODEL_420d32b41b0144b3924bd1dccda169dc"
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-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HTMLView",
-            "description": "",
-            "description_tooltip": null,
-            "layout": "IPY_MODEL_fdeb2f5d6f9e44c098b8e11ca678b5f3",
-            "placeholder": "​",
-            "style": "IPY_MODEL_28d81134a5934780941c632e7d5df3e7",
-            "value": "Downloading (…)okenizer_config.json: 100%"
-          }
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
         },
-        "c52bdd43a472471486bbbbcb96ee1a39": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "FloatProgressModel",
-          "model_module_version": "1.5.0",
-          "state": {
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-            "_model_module": "@jupyter-widgets/controls",
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-            "_view_name": "ProgressView",
-            "bar_style": "success",
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-            "orientation": "horizontal",
-            "style": "IPY_MODEL_ecc66ec306e243bfa88619ffac0a364a",
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+        "id": "bF1zZBemKrb4",
+        "outputId": "d20629e4-bf0c-4e20-abbb-46ba3539c65d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Cloning into 'AutoGPTQ'...\n",
+            "remote: Enumerating objects: 2487, done.\u001b[K\n",
+            "remote: Counting objects: 100% (825/825), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (427/427), done.\u001b[K\n",
+            "remote: Total 2487 (delta 503), reused 574 (delta 382), pack-reused 1662\u001b[K\n",
+            "Receiving objects: 100% (2487/2487), 7.48 MiB | 19.33 MiB/s, done.\n",
+            "Resolving deltas: 100% (1627/1627), done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!git clone https://github.com/PanQiWei/AutoGPTQ.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e6YsmwJo99Bf"
+      },
+      "source": [
+        "First we patch the repository to enable use_auth_token support. Don't do this if you want to use a model that doesn't require an access token. Also, this patch may become obsolete very soon so you may try without it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
         },
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-            "_view_count": null,
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-            "description": "",
-            "description_tooltip": null,
-            "layout": "IPY_MODEL_647102c6bb264a02acd51baec051f5c1",
-            "placeholder": "​",
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-            "value": " 770/770 [00:00&lt;00:00, 57.6kB/s]"
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+        "id": "PqehoDrz98vC",
+        "outputId": "6173578c-3a47-4b1f-a640-21a816c035cb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2023-07-26 20:59:49--  https://about.benjaminmarie.com/data/py/auto-gptq-patch/_utils.py\n",
+            "Resolving about.benjaminmarie.com (about.benjaminmarie.com)... 192.95.30.6\n",
+            "Connecting to about.benjaminmarie.com (about.benjaminmarie.com)|192.95.30.6|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 7224 (7.1K) [text/x-python]\n",
+            "Saving to: ‘_utils.py’\n",
+            "\n",
+            "_utils.py           100%[===================>]   7.05K  --.-KB/s    in 0s      \n",
+            "\n",
+            "2023-07-26 20:59:50 (182 MB/s) - ‘_utils.py’ saved [7224/7224]\n",
+            "\n",
+            "--2023-07-26 20:59:50--  https://about.benjaminmarie.com/data/py/auto-gptq-patch/auto.py\n",
+            "Resolving about.benjaminmarie.com (about.benjaminmarie.com)... 192.95.30.6\n",
+            "Connecting to about.benjaminmarie.com (about.benjaminmarie.com)|192.95.30.6|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 4697 (4.6K) [text/x-python]\n",
+            "Saving to: ‘auto.py’\n",
+            "\n",
+            "auto.py             100%[===================>]   4.59K  --.-KB/s    in 0s      \n",
+            "\n",
+            "2023-07-26 20:59:50 (134 MB/s) - ‘auto.py’ saved [4697/4697]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget https://about.benjaminmarie.com/data/py/auto-gptq-patch/_utils.py\n",
+        "!wget https://about.benjaminmarie.com/data/py/auto-gptq-patch/auto.py\n",
+        "\n",
+        "!mv _utils.py AutoGPTQ/auto_gptq/modeling/\n",
+        "!mv auto.py AutoGPTQ/auto_gptq/modeling/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
         },
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+        "id": "JZTifEWAX6cf",
+        "outputId": "fc1cd929-cacc-42ac-d4eb-5034e89712a3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/content/AutoGPTQ\n",
+            "Processing /content/AutoGPTQ\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting accelerate>=0.19.0 (from auto-gptq==0.3.2+cu118)\n",
+            "  Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting datasets (from auto-gptq==0.3.2+cu118)\n",
+            "  Downloading datasets-2.14.0-py3-none-any.whl (492 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m492.2/492.2 kB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from auto-gptq==0.3.2+cu118) (1.22.4)\n",
+            "Collecting rouge (from auto-gptq==0.3.2+cu118)\n",
+            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
+            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from auto-gptq==0.3.2+cu118) (2.0.1+cu118)\n",
+            "Collecting safetensors (from auto-gptq==0.3.2+cu118)\n",
+            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m54.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting transformers>=4.31.0 (from auto-gptq==0.3.2+cu118)\n",
+            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m49.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting peft (from auto-gptq==0.3.2+cu118)\n",
+            "  Downloading peft-0.4.0-py3-none-any.whl (72 kB)\n",
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+            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.19.0->auto-gptq==0.3.2+cu118) (23.1)\n",
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+            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers>=4.31.0->auto-gptq==0.3.2+cu118)\n",
+            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers>=4.31.0->auto-gptq==0.3.2+cu118)\n",
+            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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+            "\u001b[?25hCollecting multiprocess (from datasets->auto-gptq==0.3.2+cu118)\n",
+            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets->auto-gptq==0.3.2+cu118) (2023.6.0)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets->auto-gptq==0.3.2+cu118) (3.8.5)\n",
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+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->auto-gptq==0.3.2+cu118) (23.1.0)\n",
+            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->auto-gptq==0.3.2+cu118) (2.0.12)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->auto-gptq==0.3.2+cu118) (6.0.4)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->auto-gptq==0.3.2+cu118) (4.0.2)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->auto-gptq==0.3.2+cu118) (1.9.2)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->auto-gptq==0.3.2+cu118) (1.4.0)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->auto-gptq==0.3.2+cu118) (1.3.1)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->auto-gptq==0.3.2+cu118) (1.26.16)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->auto-gptq==0.3.2+cu118) (2023.7.22)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->auto-gptq==0.3.2+cu118) (3.4)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->auto-gptq==0.3.2+cu118) (2.1.3)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->auto-gptq==0.3.2+cu118) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->auto-gptq==0.3.2+cu118) (2022.7.1)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->auto-gptq==0.3.2+cu118) (1.3.0)\n",
+            "Building wheels for collected packages: auto-gptq\n",
+            "  Building wheel for auto-gptq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for auto-gptq: filename=auto_gptq-0.3.2+cu118-cp310-cp310-linux_x86_64.whl size=5667878 sha256=1e2e7e301393c5b4299ed46ca9f3f673e0565446b1899a102d3eacd1dc8898f4\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-52c3z5ek/wheels/8c/41/f4/48ea4848ab4977e74d11a4abbc2c42745c5b1d33f931e8cadf\n",
+            "Successfully built auto-gptq\n",
+            "Installing collected packages: tokenizers, safetensors, xxhash, rouge, dill, multiprocess, huggingface-hub, transformers, datasets, accelerate, peft, auto-gptq\n",
+            "Successfully installed accelerate-0.21.0 auto-gptq-0.3.2+cu118 datasets-2.14.0 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 peft-0.4.0 rouge-1.0.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0 xxhash-3.2.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "%cd AutoGPTQ\n",
+        "!pip install .\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WldO7DIZ_HDw"
+      },
+      "source": [
+        "We import all the necessary libraries:\n",
+        "\n",
+        "*Note all of them were installed as dependencies of auto-gptq*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "X_TV093r2IAB"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "from datasets import load_dataset\n",
+        "from transformers import AutoTokenizer\n",
+        "from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T0MYHD2b_XYn"
+      },
+      "source": [
+        "Set up some variables, load the tokenizer, and define a function to deal with the data used for calibration."
+      ]
+    },
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "#Replace the following with your own Hugging Face access token.\n",
+        "access_token = \"\"\n",
+        "\n",
+        "#The model we want to quantize\n",
+        "pretrained_model_dir = \"meta-llama/Llama-2-7b-chat-hf\"\n",
+        "\n",
+        "#The name of the model once quantized\n",
+        "#Note that we will only save the model, the tokenizer will remain the same\n",
+        "quantized_model_dir = \"Llama-2-7b-4bit-chat-hf\"\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, use_auth_token=access_token)\n",
+        "#I copied and edited this function from AutoGPTQ repository\n",
+        "def get_wikitext2(nsamples, seed, seqlen, model):\n",
+        "\n",
+        "    #I load the validation split since the training split is unecessary large\n",
+        "    traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='validation')\n",
+        "\n",
+        "\n",
+        "    trainenc = tokenizer(\"\\n\\n\".join(traindata['text']), return_tensors='pt')\n",
+        "\n",
+        "    random.seed(seed)\n",
+        "    np.random.seed(0)\n",
+        "    torch.random.manual_seed(0)\n",
+        "\n",
+        "    traindataset = []\n",
+        "\n",
+        "    #This is unecessary to use the entire dataset for calibration\n",
+        "    #Here I use only 128 samples\n",
+        "    for _ in range(nsamples):\n",
+        "        i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)\n",
+        "        j = i + seqlen\n",
+        "        inp = trainenc.input_ids[:, i:j]\n",
+        "        attention_mask = torch.ones_like(inp)\n",
+        "        traindataset.append({'input_ids':inp,'attention_mask': attention_mask})\n",
+        "    return traindataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6SleCPK7BZ3y"
+      },
+      "source": [
+        "Then, we can finally quantize the model and save it."
+      ]
+    },
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+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "Downloading (…)neration_config.json:   0%|          | 0.00/167 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "#Process the data that will be used for calibration\n",
+        "traindataset = get_wikitext2(128, 0, 2048, pretrained_model_dir)\n",
+        "\n",
+        "\n",
+        "quantize_config = BaseQuantizeConfig(\n",
+        "    bits=4,  # quantize model to 4-bit\n",
+        "    group_size=128,  # it is recommended to set the value to 128\n",
+        "    desc_act=False,  # set to False can significantly speed up inference but the perplexity may slightly bad\n",
+        ")\n",
+        "\n",
+        "\n",
+        "# load Llama 2\n",
+        "model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, quantize_config, use_auth_token=access_token)\n",
+        "\n",
+        "# quantize model, using traindataset for calibration\n",
+        "#this may take up to 10 minutes on Google Colab Pro\n",
+        "model.quantize(traindataset)\n",
+        "\n",
+        "# save quantized model using safetensors\n",
+        "model.save_quantized(quantized_model_dir, use_safetensors=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qHNsoFZUBgXW"
+      },
+      "source": [
+        "Now, we want to test if it still works. I'll discuss a proper evaluation method in another article."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eyMsh6pD7Fur",
+        "outputId": "484a62b9-aabd-4b14-983f-d32e15620151"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:auto_gptq.nn_modules.fused_llama_mlp:skip module injection for FusedLlamaMLPForQuantizedModel not support integrate without triton yet.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<s> Tell me about gravity. everybody knows what it is, but can you explain it in a way that makes it easy to understand?\n",
+            "Gravity is a fundamental force of nature that causes objects with mass to attract each other. It is the weakest of the four fundamental forces of nature, but it is the one that dominates at large scales, shaping the structure of the universe as we know it today.\n",
+            "Gravity is a two-way force, meaning that any two objects with mass will attract each other. The strength of the gravitational force between two objects depends on their mass and the distance between them. The greater the mass of the objects and the closer they are to each other, the stronger the gravitational force will be.\n",
+            "Gravity is a universal force that affects everything with mass, from the smallest subatomic particles to the largest galaxies. It is the force that keeps planets in orbit around their stars, causes objects to fall towards the ground, and holds galaxies together.\n",
+            "One of the most famous examples of gravity is the way that planets orbit around their stars. The gravitational force of the star pulls the planet towards it, keeping it in a stable orbit. The strength of the gravitational force depends on the mass of the star and the distance between the planet and the star.\n",
+            "Gravity is also responsible for the way that objects fall towards the ground. Any object with mass will experience a force towards the ground due\n"
+          ]
+        }
+      ],
+      "source": [
+        "#We load the quantized model\n",
+        "model = AutoGPTQForCausalLM.from_quantized(\"Llama-2-7b-4bit-chat-hf\", use_safetensors=True, device=\"cuda:0\", use_auth_token=False)\n",
+        "\n",
+        "#Your test prompt\n",
+        "prompt = \"Tell me about gravity\"\n",
+        "print(tokenizer.decode(model.generate(**tokenizer(prompt, return_tensors=\"pt\").to(model.device), max_length=300)[0]))\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
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-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "This notebook shows how to quantize Llama 2 with GPTQ.\n",
-        "It runs on Google Colab Pro. It can also run on a machine with at least 24 GB of CPU RAM and a GPU with 12 GB VRAM\n",
-        "\n",
-        "For more details check out this article:\n",
-        "[Quantization of Llama 2 with GTPQ for Fast Inference on Your Computer](https://kaitchup.substack.com/p/quantization-of-llama-2-with-gtpq)"
-      ],
-      "metadata": {
-        "id": "pX0IffauAEmI"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "We need the last version of AutoGPTQ, so we will install it from GitHub."
-      ],
-      "metadata": {
-        "id": "LlokkEwtAWEs"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!git clone https://github.com/PanQiWei/AutoGPTQ.git"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bF1zZBemKrb4",
-        "outputId": "d20629e4-bf0c-4e20-abbb-46ba3539c65d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Cloning into 'AutoGPTQ'...\n",
-            "remote: Enumerating objects: 2487, done.\u001b[K\n",
-            "remote: Counting objects: 100% (825/825), done.\u001b[K\n",
-            "remote: Compressing objects: 100% (427/427), done.\u001b[K\n",
-            "remote: Total 2487 (delta 503), reused 574 (delta 382), pack-reused 1662\u001b[K\n",
-            "Receiving objects: 100% (2487/2487), 7.48 MiB | 19.33 MiB/s, done.\n",
-            "Resolving deltas: 100% (1627/1627), done.\n"
-          ]
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-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "First we patch the repository to enable use_auth_token support. Don't do this if you want to use a model that doesn't require an access token. Also, this patch may become obsolete very soon so you may try without it."
-      ],
-      "metadata": {
-        "id": "e6YsmwJo99Bf"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!wget https://about.benjaminmarie.com/data/py/auto-gptq-patch/_utils.py\n",
-        "!wget https://about.benjaminmarie.com/data/py/auto-gptq-patch/auto.py\n",
-        "\n",
-        "!mv _utils.py AutoGPTQ/auto_gptq/modeling/\n",
-        "!mv auto.py AutoGPTQ/auto_gptq/modeling/"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "PqehoDrz98vC",
-        "outputId": "6173578c-3a47-4b1f-a640-21a816c035cb"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--2023-07-26 20:59:49--  https://about.benjaminmarie.com/data/py/auto-gptq-patch/_utils.py\n",
-            "Resolving about.benjaminmarie.com (about.benjaminmarie.com)... 192.95.30.6\n",
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-            "HTTP request sent, awaiting response... 200 OK\n",
-            "Length: 7224 (7.1K) [text/x-python]\n",
-            "Saving to: ‘_utils.py’\n",
-            "\n",
-            "_utils.py           100%[===================>]   7.05K  --.-KB/s    in 0s      \n",
-            "\n",
-            "2023-07-26 20:59:50 (182 MB/s) - ‘_utils.py’ saved [7224/7224]\n",
-            "\n",
-            "--2023-07-26 20:59:50--  https://about.benjaminmarie.com/data/py/auto-gptq-patch/auto.py\n",
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-            "Saving to: ‘auto.py’\n",
-            "\n",
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-            "\n",
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-            "\n"
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-      "metadata": {
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-          "name": "stdout",
-          "text": [
-            "/content/AutoGPTQ\n",
-            "Processing /content/AutoGPTQ\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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-            "\u001b[?25hCollecting datasets (from auto-gptq==0.3.2+cu118)\n",
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-            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->auto-gptq==0.3.2+cu118) (1.3.0)\n",
-            "Building wheels for collected packages: auto-gptq\n",
-            "  Building wheel for auto-gptq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for auto-gptq: filename=auto_gptq-0.3.2+cu118-cp310-cp310-linux_x86_64.whl size=5667878 sha256=1e2e7e301393c5b4299ed46ca9f3f673e0565446b1899a102d3eacd1dc8898f4\n",
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-            "Successfully built auto-gptq\n",
-            "Installing collected packages: tokenizers, safetensors, xxhash, rouge, dill, multiprocess, huggingface-hub, transformers, datasets, accelerate, peft, auto-gptq\n",
-            "Successfully installed accelerate-0.21.0 auto-gptq-0.3.2+cu118 datasets-2.14.0 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 peft-0.4.0 rouge-1.0.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0 xxhash-3.2.0\n"
-          ]
-        }
-      ],
-      "source": [
-        "%cd AutoGPTQ\n",
-        "!pip install .\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "We import all the necessary libraries:\n",
-        "\n",
-        "*Note all of them were installed as dependencies of auto-gptq*"
-      ],
-      "metadata": {
-        "id": "WldO7DIZ_HDw"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import random\n",
-        "import numpy as np\n",
-        "import torch\n",
-        "from datasets import load_dataset\n",
-        "from transformers import AutoTokenizer\n",
-        "from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig"
-      ],
-      "metadata": {
-        "id": "X_TV093r2IAB"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Set up some variables, load the tokenizer, and define a function to deal with the data used for calibration."
-      ],
-      "metadata": {
-        "id": "T0MYHD2b_XYn"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Replace the following with your own Hugging Face access token.\n",
-        "#This is my token (revocated, of course, never share your token online)\n",
-        "access_token = \"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\"\n",
-        "\n",
-        "#The model we want to quantize\n",
-        "pretrained_model_dir = \"meta-llama/Llama-2-7b-chat-hf\"\n",
-        "\n",
-        "#The name of the model once quantized\n",
-        "#Note that we will only save the model, the tokenizer will remain the same\n",
-        "quantized_model_dir = \"Llama-2-7b-4bit-chat-hf\"\n",
-        "\n",
-        "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, use_auth_token=access_token)\n",
-        "#I copied and edited this function from AutoGPTQ repository\n",
-        "def get_wikitext2(nsamples, seed, seqlen, model):\n",
-        "\n",
-        "    #I load the validation split since the training split is unecessary large\n",
-        "    traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='validation')\n",
-        "\n",
-        "\n",
-        "    trainenc = tokenizer(\"\\n\\n\".join(traindata['text']), return_tensors='pt')\n",
-        "\n",
-        "    random.seed(seed)\n",
-        "    np.random.seed(0)\n",
-        "    torch.random.manual_seed(0)\n",
-        "\n",
-        "    traindataset = []\n",
-        "\n",
-        "    #This is unecessary to use the entire dataset for calibration\n",
-        "    #Here I use only 128 samples\n",
-        "    for _ in range(nsamples):\n",
-        "        i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)\n",
-        "        j = i + seqlen\n",
-        "        inp = trainenc.input_ids[:, i:j]\n",
-        "        attention_mask = torch.ones_like(inp)\n",
-        "        traindataset.append({'input_ids':inp,'attention_mask': attention_mask})\n",
-        "    return traindataset"
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         },
-        {
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-          "data": {
-            "text/plain": [
-              "Downloading (…)neration_config.json:   0%|          | 0.00/167 [00:00<?, ?B/s]"
-            ],
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         }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Now, we want to test if it still works. I'll discuss a proper evaluation method in another article."
-      ],
-      "metadata": {
-        "id": "qHNsoFZUBgXW"
       }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#We load the quantized model\n",
-        "model = AutoGPTQForCausalLM.from_quantized(\"Llama-2-7b-4bit-chat-hf\", use_safetensors=True, device=\"cuda:0\", use_auth_token=False)\n",
-        "\n",
-        "#Your test prompt\n",
-        "prompt = \"Tell me about gravity\"\n",
-        "print(tokenizer.decode(model.generate(**tokenizer(prompt, return_tensors=\"pt\").to(model.device), max_length=300)[0]))\n",
-        "\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eyMsh6pD7Fur",
-        "outputId": "484a62b9-aabd-4b14-983f-d32e15620151"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "WARNING:auto_gptq.nn_modules.fused_llama_mlp:skip module injection for FusedLlamaMLPForQuantizedModel not support integrate without triton yet.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<s> Tell me about gravity. everybody knows what it is, but can you explain it in a way that makes it easy to understand?\n",
-            "Gravity is a fundamental force of nature that causes objects with mass to attract each other. It is the weakest of the four fundamental forces of nature, but it is the one that dominates at large scales, shaping the structure of the universe as we know it today.\n",
-            "Gravity is a two-way force, meaning that any two objects with mass will attract each other. The strength of the gravitational force between two objects depends on their mass and the distance between them. The greater the mass of the objects and the closer they are to each other, the stronger the gravitational force will be.\n",
-            "Gravity is a universal force that affects everything with mass, from the smallest subatomic particles to the largest galaxies. It is the force that keeps planets in orbit around their stars, causes objects to fall towards the ground, and holds galaxies together.\n",
-            "One of the most famous examples of gravity is the way that planets orbit around their stars. The gravitational force of the star pulls the planet towards it, keeping it in a stable orbit. The strength of the gravitational force depends on the mass of the star and the distance between the planet and the star.\n",
-            "Gravity is also responsible for the way that objects fall towards the ground. Any object with mass will experience a force towards the ground due\n"
-          ]
-        }
-      ]
     }
-  ]
-}
\ No newline at end of file
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/LLMs/models/llama2/Run_Llama_2_Chat_Models_on_Your_Computer.ipynb b/LLMs/models/llama2/Run_Llama_2_Chat_Models_on_Your_Computer.ipynb
index 8475cbf..0918ac5 100644
--- a/LLMs/models/llama2/Run_Llama_2_Chat_Models_on_Your_Computer.ipynb
+++ b/LLMs/models/llama2/Run_Llama_2_Chat_Models_on_Your_Computer.ipynb
@@ -1,217 +1,333 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UaHcYzEKlwI_"
+      },
+      "source": [
+        "In this notebook, we use 4-bit quantization to run Llama-7B Chat model. This code uses only 10 GB of VRAM. It can run on a free instance of Google Colab or on a local GPU (e.g., RTX 3060 12GB).\n",
+        "[More details here.](https://open.substack.com/pub/kaitchup/p/run-llama-2-chat-models-on-your-computer?r=2kp66c&utm_campaign=post&utm_medium=web)\n",
+        "\n",
+        "\n",
+        "We only need the following libraries:\n",
+        "\n",
+        "\n",
+        "*   transformers\n",
+        "*   accelerate (for device_map)\n",
+        "*   bitsandbytes (for 4-bit quantization)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZgGyWYanlvr_",
+        "outputId": "0e6d7ec6-0826-44b1-e275-06d4c696f9ae"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting transformers\n",
+            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting accelerate\n",
+            "  Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting bitsandbytes\n",
+            "  Downloading bitsandbytes-0.40.2-py3-none-any.whl (92.5 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
+            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
+            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
+            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
+            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m70.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
+            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m48.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
+            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.7.1)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.11.1)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
+            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
+            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.25.2)\n",
+            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (16.0.6)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.16)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.5.7)\n",
+            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
+            "Installing collected packages: tokenizers, safetensors, bitsandbytes, huggingface-hub, transformers, accelerate\n",
+            "Successfully installed accelerate-0.21.0 bitsandbytes-0.40.2 huggingface-hub-0.16.4 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install transformers accelerate bitsandbytes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KCdND_LSkVPg"
+      },
+      "source": [
+        "Note that to run the following code, you must have got access to Llama 2's weights and have an access token from Hugging Face. You can find instructions on the model cards on the hugging face hub: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 736,
+          "referenced_widgets": [
+            "264bcc9bf48f4692ab0eddc3bf13726c",
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+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            "text/plain": [
+              "Downloading (…)okenizer_config.json:   0%|          | 0.00/770 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1714: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
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+            "text/plain": [
+              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
+            ]
+          },
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+          "output_type": "display_data"
+        },
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+            },
+            "text/plain": [
+              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
+            ]
+          },
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+        {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (4096) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tell me about gravity. Unterscheidung between gravitational and centrifugal force.\n",
+            "Gravity is a fundamental force of nature that governs the behavior of objects with mass or energy. It is a long-range force that acts between any two objects with mass, and is always attractive in nature. Gravity is the weakest of the four fundamental forces of nature, but it is the one that dominates at large scales, shaping the structure of the universe as we know it today.\n",
+            "\n",
+            "Gravitational force is the force that acts between two objects with mass, causing them to attract each other. The strength of the gravitational force between two objects depends on their mass, the distance between them, and the gravitational constant (G). The gravitational force is always attractive, meaning that the objects will always be drawn towards each other.\n",
+            "\n",
+            "Centrifugal force, on the other hand, is a fictitious force that arises in an object rotating around a central axis. It is the force that pushes an object away from the center of rotation, and is proportional to the square of the object's distance from the center of rotation. Centrifugal force is a real force that can be observed in many everyday situations, such as when you spin a top or ride in a spinning chair.\n",
+            "\n",
+            "The key difference between gravitational and centrifugal force is the direction of the force. Gravitational force always acts between two objects with mass, and is always attractive. Centrifugal force, on the other hand, acts along the axis of rotation, and is always repulsive. In other words, gravity pulls objects towards each other, while centrifugal force pushes objects away from the center of rotation.\n",
+            "\n",
+            "Here are some key differences between gravitational and centrifugal force:\n",
+            "\n",
+            "1. Direction of force: Gravitational force always acts between two objects with mass, and is always attractive. Centrifugal force acts along the axis of rotation, and is always repulsive.\n",
+            "2. Range of force: Gravity has a long range, acting between any two objects with mass, no matter how far apart they are. Centrifugal force only acts within a limited range, typically within a few radii of the central axis of rotation.\n",
+            "3. Strength of force: The strength of the gravitational force between two objects depends on their mass, the distance between them, and the gravitational constant (G). The strength of the centrifugal force depends on the square of the object's distance from the center of rotation.\n",
+            "4. Applications: Gravity plays a crucial role in many areas of physics, including planetary motion, orbital mechanics, and the behavior of large-scale structures in the universe. Centrifugal force is used in many everyday applications, such as spinning tops, spinning chairs, and centrifuges.\n",
+            "\n",
+            "In summary, gravitational force and centrifugal force are two different types of forces that have distinct properties and applications. While gravity is a long-range attractive force that governs the behavior of large-scale structures in the universe, centrifugal force is a fictitious force that arises in objects rotating around a central axis, and is always repulsive.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "model_name = \"meta-llama/Llama-2-7b-chat-hf\"\n",
+        "prompt = \"Tell me about gravity\"\n",
+        "access_token = \"\"\n",
+        "\n",
+        "\n",
+        "\n",
+        "model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\", load_in_4bit=True,  use_auth_token=access_token)\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_auth_token=access_token)\n",
+        "model_inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda:0\")\n",
+        "\n",
+        "output = model.generate(**model_inputs)\n",
+        "\n",
+        "print(tokenizer.decode(output[0], skip_special_tokens=True))\n"
+      ]
+    }
+  ],
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-        "[More details here.](https://open.substack.com/pub/kaitchup/p/run-llama-2-chat-models-on-your-computer?r=2kp66c&utm_campaign=post&utm_medium=web)\n",
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-            "Installing collected packages: tokenizers, safetensors, bitsandbytes, huggingface-hub, transformers, accelerate\n",
-            "Successfully installed accelerate-0.21.0 bitsandbytes-0.40.2 huggingface-hub-0.16.4 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0\n"
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-        "Note that to run the following code, you must have got access to Llama 2's weights and have an access token from Hugging Face. You can find instructions on the model cards on the hugging face hub: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf\n"
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-              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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-            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
-            "  warnings.warn(\n",
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-            "Tell me about gravity. Unterscheidung between gravitational and centrifugal force.\n",
-            "Gravity is a fundamental force of nature that governs the behavior of objects with mass or energy. It is a long-range force that acts between any two objects with mass, and is always attractive in nature. Gravity is the weakest of the four fundamental forces of nature, but it is the one that dominates at large scales, shaping the structure of the universe as we know it today.\n",
-            "\n",
-            "Gravitational force is the force that acts between two objects with mass, causing them to attract each other. The strength of the gravitational force between two objects depends on their mass, the distance between them, and the gravitational constant (G). The gravitational force is always attractive, meaning that the objects will always be drawn towards each other.\n",
-            "\n",
-            "Centrifugal force, on the other hand, is a fictitious force that arises in an object rotating around a central axis. It is the force that pushes an object away from the center of rotation, and is proportional to the square of the object's distance from the center of rotation. Centrifugal force is a real force that can be observed in many everyday situations, such as when you spin a top or ride in a spinning chair.\n",
-            "\n",
-            "The key difference between gravitational and centrifugal force is the direction of the force. Gravitational force always acts between two objects with mass, and is always attractive. Centrifugal force, on the other hand, acts along the axis of rotation, and is always repulsive. In other words, gravity pulls objects towards each other, while centrifugal force pushes objects away from the center of rotation.\n",
-            "\n",
-            "Here are some key differences between gravitational and centrifugal force:\n",
-            "\n",
-            "1. Direction of force: Gravitational force always acts between two objects with mass, and is always attractive. Centrifugal force acts along the axis of rotation, and is always repulsive.\n",
-            "2. Range of force: Gravity has a long range, acting between any two objects with mass, no matter how far apart they are. Centrifugal force only acts within a limited range, typically within a few radii of the central axis of rotation.\n",
-            "3. Strength of force: The strength of the gravitational force between two objects depends on their mass, the distance between them, and the gravitational constant (G). The strength of the centrifugal force depends on the square of the object's distance from the center of rotation.\n",
-            "4. Applications: Gravity plays a crucial role in many areas of physics, including planetary motion, orbital mechanics, and the behavior of large-scale structures in the universe. Centrifugal force is used in many everyday applications, such as spinning tops, spinning chairs, and centrifuges.\n",
-            "\n",
-            "In summary, gravitational force and centrifugal force are two different types of forces that have distinct properties and applications. While gravity is a long-range attractive force that governs the behavior of large-scale structures in the universe, centrifugal force is a fictitious force that arises in objects rotating around a central axis, and is always repulsive.\n"
-          ]
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+            "_model_name": "LayoutModel",
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+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
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         }
-      ],
-      "source": [
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "model_name = \"meta-llama/Llama-2-7b-chat-hf\"\n",
-        "prompt = \"Tell me about gravity\"\n",
-        "access_token = \"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\"\n",
-        "\n",
-        "\n",
-        "\n",
-        "model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\", load_in_4bit=True,  use_auth_token=access_token)\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_auth_token=access_token)\n",
-        "model_inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda:0\")\n",
-        "\n",
-        "output = model.generate(**model_inputs)\n",
-        "\n",
-        "print(tokenizer.decode(output[0], skip_special_tokens=True))\n"
-      ]
+      }
     }
-  ]
-}
\ No newline at end of file
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}