# NVIDIA-NeMo/Nemotron

Developer Asset Hub for NVIDIA Nemotron — A one-stop resource for training recipes, usage cookbooks, datasets, and full end-to-end reference examples to build with Nemotron models

Repository: https://github.com/NVIDIA-NeMo/Nemotron
Canonical: https://ross.abutalabs.com/products/nemotron
Homepage: https://docs.nvidia.com/nemotron/latest/index.html
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: ai, fine-tuning, model-training, nemotron, nvidia, reinforcement-learning
Last push: 2026-08-21T21:29:19+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 44, longevity 23
- inputs: {"age_days": 334, "days_push": 12, "days_rel": 162, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1983, forks 404 (observed 2026-08-28T04:06:01.891902+00:00)

## What it is
NVIDIA's developer asset hub for the Nemotron family of open LLMs, providing reproducible training recipes (pretraining, SFT, RL), usage cookbooks, datasets, and end-to-end reference examples. It includes a CLI pipeline runner that submits stage-based training jobs to Slurm clusters via NeMo-Run, plus deployment guides for TensorRT-LLM, vLLM, SGLang, and NIM.

## Use cases
- fine-tune a Nemotron model with supervised fine-tuning
- run a full pretraining to RL training pipeline on a Slurm cluster
- find training recipes for hybrid Mamba-Transformer MoE models
- deploy Nemotron models with vLLM or TensorRT-LLM
- build RAG agents and multi-agent systems with Nemotron
- learn how to reproduce NVIDIA's open model training
- prepare datasets for LLM pretraining and alignment

## When to choose
- you want to train, fine-tune, or align Nemotron models with proven recipes
- you need reproducible end-to-end LLM training pipelines with transparent data and weights
- you want cookbooks and examples for deploying Nemotron on GPUs or via NIM
- you're building agentic AI applications on NVIDIA open models

## When to avoid
- you need a general-purpose training framework unrelated to Nemotron models
- you only want to run inference without training or deployment guidance
- you lack GPU or Slurm cluster resources for the training pipelines

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, machine-learning, rag, agent-framework, developer-tools
- domain: large-language-models, machine-learning, artificial-intelligence, tutorials, developer-tools
- platform: python, cli
- tags: nvidia, nemotron, training-recipes, fine-tuning, reinforcement-learning, cookbooks, jupyter-notebooks, slurm, open-models, ai-agents, linux, gpu

## Member repositories
- NVIDIA-NeMo/Nemotron (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:01.891902+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:03:24.599245+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA-NeMo/Nemotron (fetched 2026-08-28T04:06:01.891902+00:00, sha 69626fcd5645)
  - homepage: https://docs.nvidia.com/nemotron/latest/index.html (fetched 2026-08-29T10:43:11.881136+00:00, sha 171377ebdb65)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/privacy-policy (fetched 2026-08-29T10:43:11.883549+00:00, sha 5362c58d0750)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/privacy-center (fetched 2026-08-29T10:43:11.886054+00:00, sha b098377da9cc)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/terms-of-service (fetched 2026-08-29T10:43:11.887849+00:00, sha 85469b6ff0a1)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/accessibility (fetched 2026-08-29T10:43:11.890388+00:00, sha 8f61ce4143ec)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/company-policies (fetched 2026-08-29T10:43:11.892081+00:00, sha b169611fd7d6)
- Data as of 2026-08-30T08:39:29.467469+00:00.