NVIDIA Nemotron Developer Repository

Open and efficient models for agentic AI. Training recipes, deployment guides, and use-case examples for the Nemotron family.

---

🎉Nemotron 3.5 Lightning is now released — a 30B-A3B hybrid Mamba-Transformer MoE with Multi-Token Prediction, built for the high-volume execution layer of long-running agents. See the release blog, the training recipe, and the model weights.

🎉Nemotron 3 Ultra was announced at GTC San Jose 2026. The model is open-source on Hugging Face, and the training recipe is now available in this repo. To learn more, see the usage guide!

🎉Nemotron 3 Nano Omni is now released — a 30B-A3B hybrid Mamba-Transformer MoE with native text, image, video, and audio support, designed as a multimodal perception sub-agent for agentic AI. See the release blog, the training recipe, and the model weights.

---

Why Nemotron?

---

Use from Claude Code

This repo ships a Claude Code plugin called nemotron-customize that turns the step catalog under src/nemotron/steps/ into a guided, repo-native pipeline builder.

Install once:

/plugin marketplace add NVIDIA/Nemotron
/plugin install nemotron-customize@nvidia-nemotron

Then, start Claude Code from the repo root and invoke the skill:

cd /path/to/Nemotron        # repo root: must contain pyproject.toml and src/nemotron/steps/
claude

/nemotron-customize

The skill resolves all file paths against your current working directory, so it must be invoked from the Nemotron checkout root. Running it from a subdirectory will cause file reads to fail.

The skill plans the step DAG, validates artifact wiring, and emits the YAML configs needed to run the requested pipeline. See skills/nemotron-customize/SKILL.md for the full contract.

The marketplace installs only nemotron-customize. The other folders under skills/ (model knowledge bases, contributor add-* skills) stay on disk for repo browsing but are not loaded as plugins.

---

Repository Overview

nemotron/
│
├── src/nemotron/steps/      Modular building blocks for training, eval, SDG, and more
│
├── src/nemotron/recipes/    Training recipes (complete, reproducible pipelines)
│
├── usage-cookbook/          Usage cookbooks (deployment and model usage guides)
│
└── use-case-examples/       Examples of leveraging Nemotron in agentic workflows

Which section should I use?

---

What is Nemotron?

NVIDIA Nemotron is a family of open, high-efficiency multimodal models purpose-built for agentic AI.

Model Tiers:

• Nano — Optimized for edge and PC deployments
• Lightning — Fast, high-volume task execution for long-running agents
• Super — Single GPU deployment with highest throughput
• Ultra — Multi-GPU datacenter applications

Nemotron models excel at coding, math, scientific reasoning, tool calling, instruction following, and visual reasoning. Deploy across edge, single GPU, or data center environments with support for NeMo, TensorRT-LLM, vLLM, SGLang, and NIM microservices.

---

Nemotron Steps

A Nemotron step is a named, reusable unit of work that you invoke with the nemotron steps CLI. Each step packages a description of the work it performs, the artifacts it consumes and produces, and one or more named configurations that supply parameter values. Steps live under src/nemotron/steps/, and the CLI discovers them at startup.

The training recipes in the next section are composed from these steps. Run a step on its own when you want one stage, or chain steps together when you need a different pipeline shape than the published recipes.

Step Categories

The catalog covers the full training lifecycle.

• Data curation and preparation with curate/* and data_prep/*.
• Synthetic data generation (SDG) with sdg/*.
• Corpus translation with translate/*.
• Bring-your-own benchmark generation with byob/*.
• Pretraining, supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), and reinforcement learning (RL) with pretrain/*, sft/*, peft/*, and rl/*.
• Checkpoint conversion and model optimization with convert/* and optimize/*.
• Benchmark evaluation with eval/*.
• Execution-profile setup with env/*.

Documentation

• About Nemotron Steps is the entry point for the step model.
• Nemotron Steps Basics defines step, configuration, environment profile, and artifact.
• Getting Started With Steps walks through the CLI on a small example.

---

Training Recipes

The Nemotron repository provides reproducible training pipelines from raw data to deployment-ready models. These implementations reflect how large language models are actually trained: careful experimentation, validation gates, and systematic optimization.

Why Complete Pipelines?

Training a production model involves interconnected components. Isolated examples miss how stages interact. Complete pipelines show:

• How data quality affects downstream performance across pretraining, SFT, and RL
• Which training techniques actually work together, not just in theory
• Where validation gates prevent failures and maintain reproducibility
• How to balance competing objectives across stages

Because these are complete systems, you can extract specific techniques with confidence. Each component has been proven to work in context.

Each Recipe Includes

• 🎨 Synthetic Data Generation - Scripts to generate synthetic datasets using NVIDIA-NeMo/DataDesigner
• 🗂️ Data Curation - Scripts to prepare training data using NVIDIA NeMo Curator for scalable data processing, filtering, and quality enhancement
• 🔁 Training - Complete training loops with hyperparameters using:
  • NVIDIA-NeMo/Megatron-Bridge for Megatron models
  • NVIDIA-NeMo/Automodel for HuggingFace models
  • NVIDIA-NeMo/NeMo-RL when RL is needed
  • Includes GPU-accelerated last-mile data processing (tokenization + optional sequence packing) for optimal training efficiency
• 📊 Evaluation - Benchmark evaluation on standard suites using NVIDIA NeMo Evaluator
• 📖 Documentation - Detailed explanations of each stage

Available Recipes

Nemotron 3 Ultra

A training recipe for NVIDIA's largest Nemotron 3 model — a 550B-A55B hybrid Mamba-Attention Mixture-of-Experts Transformer with LatentMoE and multi-token prediction (MTP), pretrained in NVFP4 and extended to 1M-token context for datacenter-scale agentic reasoning.

Open-Source Data Only: These recipes train exclusively on the open-sourced subset of training data. Results will differ from the tech report benchmarks, which used additional proprietary data. Use these recipes as reference implementations to apply the methodology with your own data.

Model Specifications:

• 550B total / 55B active parameters (MoE)
• Hybrid Mamba-Attention architecture with LatentMoE + two shared-weight MTP layers
• 20T pretraining tokens in NVFP4, two-phase data curriculum
• Up to 1M (1,048,576) context length
• Full program: Pretrain → SFT → RLVR → MOPD → MTP Boosting (this recipe covers Pretrain → SFT)

What You Can Extract:

• Two-phase pretraining data mixture (tech-report Figure 4) over the open Nemotron datasets
• Ray-based data prep: tokenize raw datasets → Megatron bin/idx (pretrain) and pack chat data → Parquet (SFT)
• New open pretraining datasets: Specialized-v1.2 (Multiple-Choice / Generative / Fact-Seeking / Moral-Scenarios) and Legal-v1
• Stage-local container builds (Day-0 Megatron-Bridge) for both pretrain and SFT
• Megatron-Bridge training at Ultra scale (TP=2 / PP=12 / EP=32 pretrain, PP=6 SFT)

Resources:

• Training Guide
• Usage Guide
• Model Weights (BF16)

Nemotron 3 Super

A complete training recipe for the frontier Hybrid Mamba Latent Mixture-of-Experts Transformer model with state-of-the-art reasoning, coding, and agentic capabilities.

Open-Source Data Only: These recipes train exclusively on the open-sourced subset of training data. Results will differ from the tech report benchmarks, which used additional proprietary data. Use these recipes as reference implementations to apply the methodology with your own data.

Model Specifications:

• 120B total / 12B active parameters
• Multi-stage RL pipeline: 3× RLVR + 2× SWE-RL + RLHF across 21 reward environments
• Asynchronous GRPO with decoupled training and inference

What You Can Extract:

• Large-scale pretraining with data curriculum
• Multi-domain SFT pipeline
• Multi-environment RLVR with 21 simultaneous reward environments
• SWE-RL with container-isolated sandbox execution
• GenRM-based RLHF with principle-following rewards
• Asynchronous GRPO at 1K GPU scale

Resources:

• Training Guide
• Tech Report
• Model Weights (Instruct)

Nemotron 3.5 Lightning

A complete training recipe for the 30B-A3B hybrid Mamba-Transformer MoE with Multi-Token Prediction, built for fast, accurate specialized task execution in long-running agents. Weights, data, and recipes are released under OpenMDW-1.1.

Open-Source Data Only: These recipes train exclusively on the open-sourced subset of training data. Results will differ from the published benchmarks, which used additional proprietary data. Use these recipes as reference implementations to apply the methodology with your own data.

Model Specifications:

• 30B total parameters, 3B active per forward pass
• Hybrid Mamba-Transformer with sparse MoE and Multi-Token Prediction (MTP)
• Ships with DSpark and DFlash draft models for speculative decoding, plus an NVFP4 checkpoint
• Optimized for the high-volume execution layer of always-on agents: tool calls, result validation, and subagent delegation

What You Can Extract:

• Pretraining with MTP heads (repeated-layer MTP, 0.3 loss scaling)
• Packed-sequence SFT with the Lightning chat template
• Multi-environment RLVR with GRPO via NeMo Gym, MTP heads kept training
• Gym-native benchmark evaluation (GPQA, SciCode, and the published reference suite)
• NVFP4 quantization with four-over-six PTQ + Quantization-Aware Distillation (QAD)
• File-based (manifest) or W&B artifact lineage across all stages

Resources:

• Release Blog
• Training Guide
• NeMo-RL Lightning Guide
• DGX Station Customization Recipes
• Quantization Guide (PTQ + QAD)
• Model Weights (Base)
• Model Weights (Instruct)
• Model Weights (NVFP4)

Nemotron 3 Nano

A complete training recipe for the open, efficient Mixture-of-Experts hybrid Mamba-Transformer model optimized for agentic reasoning.

Open-Source Data Only: These recipes train exclusively on the open-sourced subset of training data. Results will differ from the tech report benchmarks, which used additional proprietary data. Use these recipes as reference implementations to apply the methodology with your own data.

Model Specifications:

• 31.6B total parameters, 3.6B active per forward pass
• 25 trillion pretraining tokens with curriculum learning
• Up to 1M context length
• 3.3x higher inference throughput than similarly sized models

What You Can Extract:

• Curriculum-based pretraining with two-phase data mixture
• Long-context extension via CPT methodology
• Multi-domain SFT with 12+ data sources
• InfinityByte cross-domain code synthesis
• Tool-calling fine-tuning and budget-controlled reasoning
• Multi-environment RLVR with GRPO
• GenRM reward modeling with circular comparison
• DPO for tool hallucination reduction

Resources:

• Training Guide
• Tech Report
• Model Weights (Base)
• Model Weights (Instruct)
• Model Weights (FP8)

Nemotron 3 Nano Omni

A multimodal training recipe for the 30B-A3B hybrid Mamba-Transformer Mixture-of-Experts model. Native support for text, image, video, and audio in a single decoder, designed as a perception sub-agent for agentic AI.

Open-Source Data Only: These recipes train exclusively on the open-sourced subset of training data (e.g., CORD-v2 for SFT, public MMPR / MMPR-Tiny for RL). Results will differ from the release benchmarks, which used additional internal datasets. Use these recipes as reference implementations to apply the methodology with your own data.

Model Specifications:

• 30B total / 3B active parameters (A3B MoE)
• Hybrid architecture: Mamba layers (sequence/memory efficiency) + transformer layers (reasoning), with a unified text decoder
• Native modalities: text, image, video, audio
• Vision encoder: C-RADIOv4-H · Audio encoder: NVIDIA Parakeet · Video pipeline: 3D convolutions + Efficient Video Sampling (EVS)
• Context length: progressively scaled 16K → 49K → 262K
• Best-in-class on MMlongbench-Doc, OCRBenchV2; leading on WorldSense, DailyOmni, VoiceBench
• Up to ~9.2× greater video-reasoning system capacity, ~7.4× on multi-document workloads vs. comparable open omni models
• License: NVIDIA Nemotron Open Model License (enterprise-friendly, on-prem and any deployment)

What You Can Extract:

• Multimodal SFT pipeline using Megatron-Bridge with the Valor32k recipe family (open-dataset CORD-v2 default + Valor32k variants)
• Progressive context scaling: 16K → 49K → 262K
• Multimodal preference optimization (MPO) on the public MMPR dataset
• Text-only GRPO continuation of alignment via NeMo-RL
• Vision GRPO on MMPR-Tiny
• Inline NVIDIA stack: Megatron-Bridge for SFT, NeMo-RL (nano-v3-omni branch with the omni vllm fork as a submodule) for RL
• Cookbook-style end-to-end recipe (build → data prep → SFT → RL → eval) reproducing the release training stages

Resources:

• Training Guide
• Release Blog
• Model Weights (BF16)
• Image Training Data
• Upstream pre-training recipe: NVIDIA-NeMo/Megatron-Bridge nemotron_3_omni
• Upstream RL recipe: NVIDIA-NeMo/RL nano-v3-omni

---

Usage Cookbooks

Practical deployment and model usage guides for Nemotron models.

---

Use Case Examples

End-to-end examples demonstrating practical applications in the use-case-examples/ directory:

• Agentic Workflows — Multi-step AI agents with planning, context management, and external tools
• RAG Systems — Pipelines combining retrieval with Nemotron models for grounded outputs
• Tool Integration — Structured tool calling, function execution, and data enrichment
• Production Patterns — Scalability, monitoring, and deployment architectures

---

Nemotron Open Datasets

More than just weights, recipes, and libraries: Nemotron is committed to opening data across many domains, training phases, and use cases.

Nemotron Data Catalogue

A comprehensive collection of NVIDIA Nemotron datasets spanning pre-training, post-training, reinforcement learning, multimodal, safety, and domain-specific applications. These openly available datasets power the Nemotron family of models for agentic AI development.

---

Code

Datasets for training code generation, competitive programming, and software engineering capabilities across multiple programming languages.

Dataset	Usage	License	Model(s)	Description
Nemotron-CC-Code-v1	Pre-training	NVIDIA Data Agreement	Nemotron 3 Nano	427.9B tokens from Common Crawl code pages using Lynx + LLM pipeline
Nemotron-Pretraining-Code-v1	Pre-training	NVIDIA Data Agreement	Nemotron Nano 2	GitHub-sourced code corpus for Nemotron Nano 2
Nemotron-Pretraining-Code-v2	Pre-training	NVIDIA Data Agreement	Nemotron 3 Nano	Updated GitHub code + synthetic QA with STEM reasoning
Nemotron-Cascade-RL-SWE	RL Training	CC-BY-4.0	Nemotron 3	SWE code repair from SWE-Bench, SWE-Smith, R2E-Gym
Nemotron-Competitive-Programming-v1	SFT	CC-BY-4.0	Nemotron 3	2M+ Python and 1M+ C++ samples across 34K competitive programming questions
OpenCodeReasoning	SFT	CC-BY-4.0	OpenCode-Nemotron	735K Python samples across 28K competitive programming questions
OpenCodeReasoning-2	SFT	CC-BY-4.0	OpenCode-Nemotron	2.5M samples (1.4M Python, 1.1M C++) with code completion and critique
Scoring-Verifiers	Evaluation	CC-BY-4.0	—	Benchmark for test case generation and code reward models

---

Math

Mathematical reasoning datasets ranging from pre-training corpora to advanced problem-solving with chain-of-thought and tool-integrated reasoning. Includes the AIMO-2 competition winning dataset.

Dataset	Usage	License	Model(s)	Description
Nemotron-CC-Math-v1	Pre-training	NVIDIA Data Agreement	Nemotron Nano 2, Nemotron 3 Nano	133B-token math dataset from Common Crawl using Lynx + LLM pipeline
Nemotron-Math-Proofs-v1	SFT	CC-BY-4.0	Nemotron 3 Nano	Mathematical proofs dataset for Nemotron 3 post-training
Nemotron-Math-v2	SFT	CC-BY-4.0	Nemotron 3	347K samples and 7M reasoning trajectories for Deeper Math Reasoning
Nemotron-CrossThink	RL Training	CC-BY-4.0	Nemotron 3	Multi-domain QA with MCQ and open-ended formats for verifiable rewards
OpenMathReasoning	SFT	CC-BY-4.0	OpenMath-Nemotron	5.68M samples, 306K problems from AoPS with CoT/TIR (AIMO-2 winner)

---

Science / STEM

Scientific reasoning datasets covering chemistry, physics, and general STEM domains for training models on scientific question answering and reasoning.

Dataset	Usage	License	Model(s)	Description
Nemotron-Science-v1	SFT	CC-BY-4.0	Nemotron 3 Nano	Synthetic science reasoning (MCQA + chemistry RQA)

---

General / Web

Large-scale web-crawled and curated datasets for pre-training and post-training, including multilingual data and general instruction-following capabilities.

Dataset	Usage	License	Model(s)	Description
Nemotron-CC-v2.1	Pre-training	NVIDIA Data Agreement	Nemotron 3 Nano	2.5T tokens English web data with synthetic rephrases and translations
Nemotron-CC-v2	Pre-training	NVIDIA Data Agreement	Nemotron Nano 2	6.6T tokens quality-filtered Common Crawl with multilingual Q&A
Nemotron-Pretraining-Dataset-sample	Pre-training (Sample)	NVIDIA Data Agreement	—	Sample subset of Nemotron pre-training corpus for experimentation
Llama-Nemotron-Post-Training-Dataset	SFT + RL	CC-BY-4.0	Llama-Nemotron Ultra/Super/Nano	Math, code, reasoning data (2.2M math, 500K code)
Nemotron-Post-Training-Dataset-v1	SFT	CC-BY-4.0	Llama-3.3-Nemotron-Super-49B-v1.5	Math, code, STEM, tool calling
Nemotron-Post-Training-Dataset-v2	SFT + RL	CC-BY-4.0	Llama-Nemotron	Multilingual extension (Spanish, French, German, Italian, Japanese)
Nemotron-3-Nano-RL-Training-Blend	RL Training	CC-BY-4.0	Nemotron-3-Nano-30B-A3B	Curated multi-domain blend for Nemotron 3 Nano
Nemotron-RL-knowledge-web_search-mcqa	RL Training	ODC-BY-1.0	Nemotron 3	Web search and multiple-choice QA tasks for NeMo Gym

---

Chat / Instruction Following

Datasets for training conversational AI with strong instruction-following capabilities, structured output generation, and multi-turn dialogue.

Dataset	Usage	License	Model(s)	Description
Nemotron-Instruction-Following-Chat-v1	SFT	CC-BY-4.0	Nemotron 3 Nano	Multi-turn chat and structured output generation
Nemotron-RL-instruction_following	RL Training	ODC-BY-1.0	Nemotron 3	Verifiable instruction adherence from WildChat-1M + Open-Instruct
Nemotron-RL-instruction_following-structured_outputs	RL Training	ODC-BY-1.0	Nemotron 3	JSON schema-constrained output formatting tests
Nemotron-Cascade-RL-Instruction-Following	RL Training	ODC-BY-1.0	Nemotron 3	108K samples for instruction-following RL

---

Agentic / Tool Use

Datasets for training AI agents with tool calling, multi-step workflows, and agentic reasoning capabilities.

Dataset	Usage	License	Model(s)	Description
Nemotron-Agentic-v1	SFT	CC-BY-4.0	Nemotron 3 Nano	Multi-turn trajectories for conversational tool use and agentic workflows
Nemotron-RL-agent-workplace_assistant	RL Training	ODC-BY-1.0	Nemotron 3	Workplace assistant agent tasks for NeMo Gym

---

Alignment / Reward Modeling

Human preference and reward modeling datasets for RLHF, SteerLM training, and model alignment. Powers top-performing reward models on RM-Bench and JudgeBench.

Dataset	Usage	License	Model(s)	Description
HelpSteer3	Reward Modeling	CC-BY-4.0	Nemotron 3 Nano, Llama-Nemotron Super 49B	40K+ samples; top on RM-Bench/JudgeBench with preference, feedback, edit-quality
HelpSteer2	Reward Modeling	CC-BY-4.0	Nemotron-4-340B-Reward, Llama-3.1-Nemotron-70B-Reward	21K samples with 5 attributes
HelpSteer	SteerLM Training	CC-BY-4.0	Nemotron-4 SteerLM	37K samples (helpfulness, correctness, coherence, complexity, verbosity)
Daring-Anteater	SFT/RLHF	CC-BY-4.0	Nemotron-4-340B-Instruct	Instruction tuning dataset; synthetic subsets + FinQA, wikitablequestions
sft_datablend_v1	SFT	CC-BY-4.0	—	SFT data blend for RLHF pipeline

---

Vision-Language / Multimodal

High-quality VLM training data for document intelligence, OCR, image reasoning, video QA, and chain-of-thought visual understanding.

Dataset	Usage	License	Model(s)	Description
Nemotron-VLM-Dataset-v2	VLM Training	CC-BY-4.0 (some CC-BY-SA-4.0)	Nemotron VLM	8M samples for OCR, image reasoning, video QA with chain-of-thought
Llama-Nemotron-VLM-Dataset-v1	VLM Training	CC-BY-4.0 (some CC-BY-SA-4.0)	Llama-3.1-Nemotron-Nano-VL-8B	3M samples for visual question answering and captioning

---

Physical AI / Robotics

Datasets for embodied reasoning, physical common sense, and robotic manipulation. Powers Cosmos-Reason1 for physical AI applications.

Dataset	Usage	License	Model(s)	Description
Cosmos-Reason1-SFT-Dataset	SFT	CC-BY-4.0	Cosmos-Reason1-7B	Video-text pairs for robotics, ego-centric demos, AV reasoning
Cosmos-Reason1-RL-Dataset	RL Training	CC-BY-4.0	Cosmos-Reason1-7B	RL data for physical common sense and embodied reasoning
Cosmos-Reason1-Benchmark	Evaluation	CC-BY-4.0	—	Benchmark for embodied reasoning (robotics, HoloAssist, AV)
PhysicalAI-Robotics-Manipulation-Augmented	Training	CC-BY-4.0	—	1K Franka Panda demos with Cosmos Transfer1 domain augmentation

---

Autonomous Vehicles

Multi-sensor driving data and synthetic scenarios for training and validating autonomous vehicle systems.

Dataset	Usage	License	Model(s)	Description
PhysicalAI-Autonomous-Vehicles	Training	NVIDIA AV Dataset License	—	1,700 hours multi-sensor data from 25 countries, 306K clips
PhysicalAI-Autonomous-Vehicle-Cosmos-Drive-Dreams	SDG	CC-BY-4.0	Cosmos	81K synthetic videos with LiDAR and HD-map annotations
PhysicalAI-Autonomous-Vehicle-Cosmos-Synthetic	SDG	CC-BY-4.0	Cosmos	Cosmos-generated synthetic driving scenarios
PhysicalAI-Autonomous-Vehicles-NuRec	Reconstruction	NVIDIA AV Dataset License	—	NuScenes-based reconstruction data

---

Synthetic Personas / Data Generation

Privacy-safe synthetic personas grounded in real-world demographics for sovereign AI development and synthetic data generation pipelines.

Dataset	Usage	License	Model(s)	Description
Nemotron-Personas-USA	SDG	CC-BY-4.0	NeMo Data Designer	1M US personas grounded in Census demographics
Nemotron-Personas-Japan	SDG	CC-BY-4.0	NeMo Data Designer	1M Japanese personas aligned with regional statistics
Nemotron-Personas-India	SDG	CC-BY-4.0	NeMo Data Designer	3M Indian personas for sovereign AI development
Nemotron-Personas	SDG	CC-BY-4.0	NeMo Data Designer	100K US personas with 22 fields aligned to Census data

---

Privacy / PII Detection

Synthetic datasets for training named entity recognition models to detect and redact personally identifiable information.

Dataset	Usage	License	Model(s)	Description
Nemotron-PII	NER Training	CC-BY-4.0	GLiNER-PII	100K synthetic records with 55+ PII/PHI entity types

---

Safety / Content Moderation

Content safety datasets for training guardrail models covering comprehensive risk taxonomies. Powers NemoGuard content safety models.

Dataset	Usage	License	Model(s)	Description
Aegis-AI-Content-Safety-Dataset-1.0	Content Moderation	CC-BY-4.0	NemoGuard Permissive/Defensive	11K annotated interactions covering 13 risk categories
Aegis-AI-Content-Safety-Dataset-2.0	Content Moderation	CC-BY-4.0	Llama-3.1-NemoGuard-8B-ContentSafety	Extended safety dataset with 23 violation categories
Nemotron-Content-Safety-Audio-Dataset	Audio Safety	CC-BY-4.0	—	1.9K audio files from Aegis 2.0 with accent diversity

---

RAG / Conversational QA

Training and evaluation data for retrieval-augmented generation and conversational question answering. Powers ChatQA models.

Dataset	Usage	License	Model(s)	Description
ChatRAG-Bench	Evaluation	Other (derived)	—	Benchmark across 10 datasets for document QA and unanswerable detection
ChatQA-Training-Data	SFT	Other (derived)	ChatQA-1.5	Training data for ChatQA models from multiple sources
ChatQA2-Long-SFT-data	SFT	Other (derived)	ChatQA-2	128K long-context training data for ChatQA-2

---

Biology / Drug Discovery

Protein sequence data for training biological foundation models.

Dataset	Usage	License	Model(s)	Description
esm2_uniref_pretraining_data	Pre-training	CC-BY-4.0	ESM2-nv	188M protein sequences for ESM2

---

3D / Spatial Intelligence

Testing and synthetic data for 3D reconstruction, video generation, and spatial understanding models.

Dataset	Usage	License	Model(s)	Description
Lyra-Testing-Example	Evaluation	CC-BY-4.0	Lyra	Testing examples for Lyra generative 3D reconstruction
PhysicalAI-SpatialIntelligence-Lyra-SDG	SDG	CC-BY-4.0	Lyra	Synthetic data for spatial intelligence models
GEN3C-Testing-Example	Evaluation	CC-BY-4.0	GEN3C	Testing examples for GEN3C video generation
ChronoEdit-Example-Dataset	Evaluation	CC-BY-4.0	ChronoEdit	Temporal reasoning examples for image editing

---

💡 Feature Requests & Ideas

Have an idea for improving Nemotron models? Create a Discussion topic for it!

If you have a feature request, feel free to open an Issue and tag it as enhancement.

Your feedback helps shape the future of Nemotron models!

---

Documentation

• Nemotron 3 Super Training Guide – frontier model training recipe
• Nemotron 3 Nano Training Guide – efficient model training recipe
• NeMo-Run Configuration – execution profiles and job orchestration
• Data Preparation – data preparation module
• Contributing Guidelines – how to contribute
• Changelog – version history

---

Contributing

We welcome contributions: examples, recipes, or other tools. Please read the Contributing Guidelines before submitting pull requests.

---

Security

To report any vulnerabilities, please reach out to security@nvidia.com

---

License

Apache 2.0 License — see LICENSE for details.

---

NVIDIA Nemotron — Open and efficient models for agentic AI.