Open and efficient models for agentic AI. Reproducible training pipelines with transparent data, techniques, and weights.
// Install the Nemotron training recipes
$ git clone https://github.com/NVIDIA-NeMo/Nemotron
$ cd Nemotron && uv sync
// Run a tiny SFT job on your cluster
$ uv run nemotron steps run sft/automodel -c tiny --run YOUR-CLUSTER
// Run the Nano3 pipeline stage by stage
$ uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER
$ uv run nemotron nano3 pretrain --run YOUR-CLUSTER
$ uv run nemotron nano3 data prep sft --run YOUR-CLUSTER
$ uv run nemotron nano3 sft --run YOUR-CLUSTER
$ uv run nemotron nano3 data prep rl --run YOUR-CLUSTER
$ uv run nemotron nano3 rl --run YOUR-CLUSTERNote: The
--run YOUR-CLUSTERflag submits jobs to your configured Slurm cluster via NeMo-Run. See Execution through NeMo-Run for setup instructions.
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:::{grid-item-card} Deployment Guides :link: deployment-guides :link-type: doc
Deployment guides for Nemotron models: TensorRT-LLM, vLLM, SGLang, NIM, and Hugging Face. :::
:::{grid-item-card} Sample Applications :link: application-examples :link-type: doc
End-to-end applications: RAG agents, ML agents, and multi-agent systems. :::
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:::{grid-item-card} Translation :link: translation/index :link-type: doc
Translate JSONL or Parquet corpora with translate/nemo_curator, NeMo Curator
backends, and optional FAITH quality scoring.
:::
:::{grid-item-card} Build MCQ Benchmarks :link: build-benchmarks/index :link-type: doc
Generate and translate custom multiple-choice benchmarks with byob/mcq.
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:::{grid-item-card} Data Curation :link: curate/index :link-type: doc
Filter JSONL text with curate/nemo_curator before translation or training data preparation.
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:::{grid-item-card} Synthetic Data Generation :link: sdg/index :link-type: doc
Use sdg/data_designer to produce SFT, tool-use, and preference datasets.
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:::{grid-item-card} Model Evaluation :link: model-eval/index :link-type: doc
Evaluate hosted endpoints or checkpoints with eval/model_eval.
:::
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:::{grid-item-card} Nemotron 3 Nano :link: nemotron/nano3/README :link-type: doc
31.6B total / 3.6B active parameters, 25T tokens, up to 1M context. Hybrid Mamba-Transformer with sparse MoE.
Stages: Pretraining → SFT → RL :::
:::{grid-item-card} Nemotron 3 Omni :link: nemotron/omni3/README :link-type: doc
GA-checkpoint multimodal post-training recipe with stage-local container builds and a three-step RL stack.
Stages: SFT → RL MPO → RL text → RL vision → Eval :::
:::{grid-item-card} Embedding Fine-Tuning :link: nemotron/embed/README :link-type: doc
Fine-tune Llama-Nemotron-Embed-1B-v2 on domain-specific data with synthetic data generation, evaluation, and NIM deployment.
Stages: SDG → Data Prep → Finetune → Eval → Export → Deploy :::
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Nemotron keeps data-producing recipes separate from model-family training recipes:
| Path | Purpose | Example |
|---|---|---|
src/nemotron/recipes/data/curation/ |
Filter, dedup, and curate existing corpora | Nemotron-CC |
src/nemotron/recipes/data/sdg/ |
Generate synthetic datasets that can feed multiple families | Long-document SDG feeding Omni3 SFT |
src/nemotron/recipes/<family>/ |
Family-specific training, RL, evaluation, and model lifecycle commands | Nano3, Omni3 |
Each recipe family has its own stage layout, and all of them can be tracked through artifact lineage:
| Family | Stage layout |
|---|---|
| Nano3 | Pretraining → SFT → RL |
| Omni3 | SFT → RL MPO → RL text → RL vision → Eval |
| Super3 | Pretraining → SFT → RL → Quantization → Eval |
| Embed | SDG → Data Prep → Finetune → Eval → Export → Deploy |
| Open Models | Transparent training data, techniques, and weights for community innovation |
| Compute Efficiency | Model pruning enabling higher throughput via TensorRT-LLM |
| High Accuracy | Built on frontier open models with human-aligned reasoning |
| Flexible Deployment | Deploy anywhere: edge, single GPU, or data center with NIM |
- End-to-end pipelines from raw data to deployment-ready models
- Artifact lineage via W&B from data to model
- Built on NVIDIA's NeMo stack (Megatron-Bridge, NeMo-RL)
- Reproducible with versioned configs, data blends, and checkpoints
- Tech Report – Nemotron 3 Nano methodology
- Model Weights – pre-trained checkpoints on HuggingFace
- Pre-training Datasets – open pre-training data
- Post-training Datasets – SFT and RL data
- Artifact Lineage – W&B integration guide
- Model training steps – SFT, PEFT, RL, and optimization with
nemotron step run
:caption: Nemotron
:hidden:
Home <self>
application-examples.md
deployment-guides.md
:caption: Nemotron Step Basics
:hidden:
About <steps/index.md>
Basics <steps/basics.md>
Getting Started <steps/getting-started.md>
Airgap Environment <steps/airgap.md>
:caption: Data Curation
:hidden:
About <curate/index.md>
Getting Started <curate/getting-started.md>
Tasks <curate/how-to/index.md>
Reference <curate/reference/index.md>
:caption: Synthetic Data Generation
:hidden:
About <sdg/index>
Getting Started <sdg/getting-started>
Tips for Using Agents <sdg/using-skills>
Planning <sdg/planning>
Tasks <sdg/how-to/index>
Reference <sdg/reference/index>
:caption: Translation
:hidden:
About <translation/index.md>
Getting Started <translation/getting-started.md>
Tips for Using Agents <translation/using-skills.md>
translation/explanation/index.md
Tasks <translation/how-to/index.md>
Reference <translation/reference/index.md>
:caption: Build MCQ Benchmarks
:hidden:
About <build-benchmarks/index.md>
Getting Started <build-benchmarks/getting-started.md>
Concepts <build-benchmarks/explanation/index.md>
Tasks <build-benchmarks/how-to/index.md>
Reference <build-benchmarks/reference/index.md>
:caption: Model Training
:hidden:
About <train-models/index.md>
Getting Started <train-models/getting-started.md>
Tips for Using Agents <train-models/using-skill.md>
Concepts <train-models/explanation/index.md>
Tasks <train-models/how-to/index.md>
Reference <train-models/reference/index.md>
:caption: Model Evaluation
:hidden:
About <model-eval/index.md>
Getting Started <model-eval/getting-started.md>
Tips for Using Agents <model-eval/using-skills.md>
Concepts <model-eval/explanation/index.md>
Tasks <model-eval/how-to/index.md>
Reference <model-eval/reference/index.md>
:caption: Training Recipes
:hidden:
Nemotron 3 Nano <nemotron/nano3/README.md>
Nemotron 3 Omni <nemotron/omni3/README.md>
Nemotron 3 Super <nemotron/super3/README.md>
Llama Nemotron Embed <nemotron/embed/README.md>
nemotron/artifacts.md
:caption: Nemotron Kit
:hidden:
nemotron/kit.md
nemotron/nvidia-stack.md
nemo_runspec/package-readme.md
nemo_runspec/nemo-run.md
nemo_runspec/omegaconf.md
nemo_runspec/artifacts.md
nemotron/wandb.md
nemotron/cli.md
nemotron/data-prep.md
nemotron/xenna-observability.md
:caption: Data Recipes
:hidden:
nemotron/data/curation/nemotron-cc.md
nemotron/data/sdg/long-document.md
:caption: Architecture
:hidden:
architecture/README.md
architecture/design-philosophy.md
architecture/cli-architecture.md
runspec/v1/spec.md