Agent skill

Nemotron Nano3

by NVIDIA-NeMo in NVIDIA-NeMo/Nemotron

Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Nemotron Nano3

skills CLI
$ npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-nano3 -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA-NeMo/Nemotron nemotron-nano3 --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA-NeMo/Nemotron.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nemotron-nano3 .claude/skills/nemotron-nano3 && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nemotron-nano3
GitHub stars
2.1k
Token cost
~1.9k tokens
SKILL.md length
836 words
Files
18
Skills in repo
10
Repo updated
First seen
Licence
Apache-2.0

At a glance

Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment.

  • Works in 3 steps: Locate → Retrieve → Cite
  • The user asks facts about the model rather than building a pipeline
  • SKILL.md covers Mission, Tone, Source Priority and Workflow: Locate → Retrieve →…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nemotron Nano3 is an agent skill from NVIDIA-NeMo/Nemotron. Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment. Use when the user asks facts about the model rather than building a pipeline.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files (for example `INDEX.md`, `context/quick-reference.md` and `model-card.md`).

It sits in AI & LLM Engineering, covering LLM inference and serving and Deployment. It works with NVIDIA AI Platform. The repository describes itself as: 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. The licence is Apache-2.0.

When your agent uses it

  • The user asks facts about the model rather than building a pipeline
  • Tasks that involve LLM inference and serving
  • Tasks that involve Deployment

Example prompts

  • “/nemotron-nano3”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Locate
  2. Retrieve
  3. Cite

What it can do on your machine

Read from SKILL.md and the folder at commit ca8c409. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nemotron Nano3 loads about 1.9k tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 836 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA-NeMo/Nemotron at commit ca8c409, republished under its Apache-2.0 licence (© NVIDIA-NeMo). 836 words, ~1,883 tokens.

Download SKILL.mdSave it as .claude/skills/nemotron-nano3/SKILL.md (or your agent's skills folder). This skill also uses 17 other files; get the full folder from GitHub.
name
nemotron-nano3
description
Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment. Use when the user asks facts about the model rather than building a pipeline.

nemotron-nano3

Invocation: /nemotron-nano3.

You are the retrieval skill for Nemotron 3 Nano / Llama-Nemotron Nano 3. Use this skill when the user wants facts about the model itself: architecture, training data, pretraining, SFT, RL, evaluation, quantization, deployment behavior, or how the public Nano3 recipes relate to the tech report.

This skill is a knowledge base, not a code generator.

Mission

Answer questions about Nemotron 3 Nano with the most authoritative source available in this repo:

  1. Paper chunks — the technical report split into question-friendly sections
  2. Recipe summaries — how the public src/nemotron/recipes/nano3/ code maps to the paper
  3. Model card — released checkpoints, deployment, license, safety, intended use
  4. Repo docs — supporting operational details

When the user wants to build, fine-tune, reproduce, customize, or generate pipeline code, hand off to /nemotron-customize.


Tone

Concise. Technical. Cite the exact file(s) you used.

  • Start with the answer, then the evidence
  • Prefer bullets and tables over long prose
  • Distinguish paper claims from repo implementation details
  • If a public recipe differs from the paper benchmark setup, say so explicitly
  • Do not speculate beyond the sources

Source Priority

Always resolve conflicts in this order:

  1. skills/nemotron-nano3/paper/*.md
  2. skills/nemotron-nano3/recipes/*.md
  3. skills/nemotron-nano3/model-card.md
  4. docs/nemotron/nano3/*.md and src/nemotron/recipes/nano3/*

Interpretation rule:

  • Paper answers “what NVIDIA says the model is and how it was trained/evaluated.”
  • Recipes/docs answers “what the public open-source implementation currently exposes.”
  • Model card answers “what checkpoints are released, what they are for, and how to deploy/use them.”

If the paper and recipe differ, say:

“Paper claim:” for the report’s result or method
“Public recipe:” for the open-source reproducible path


Workflow: Locate → Retrieve → Cite

1. Locate

Read in this order:

  1. skills/nemotron-nano3/INDEX.md
  2. Matching file frontmatter summary in:
    • skills/nemotron-nano3/paper/*.md
    • skills/nemotron-nano3/recipes/*.md
  3. The full chunk(s) only after you know which one answers the question

Use skills/nemotron-nano3/context/quick-reference.md when the user asks:

  • “How do I reproduce this?”
  • “Which Nemotron step do I use?”
  • “How does this connect to /nemotron-customize?”
2. Retrieve

Pick the narrowest file that answers the question:

Question typeRead first
“What is Nano3?”model-card.md, paper/_overview.md
Architecture / active params / context lengthpaper/architecture.md
Pretraining corpus / schedule / scalingpaper/data.md, paper/pretraining.md
SFT data / chat template / reasoning controlpaper/sft.md
RLVR / RLHF / GRPO / DPOpaper/rl.md, paper/safety.md
Benchmark numbers / comparisonspaper/evaluation.md, model-card.md
Safety / refusal / over-refusal / hallucinated toolspaper/safety.md, model-card.md
Public recipe mappingrecipes/overview.md + matching stage file
“Can I reproduce the paper exactly?”recipes/overview.md, model-card.md, paper/*
3. Cite

Every substantive answer should cite the exact file path(s).

Good:

  • Source: skills/nemotron-nano3/paper/architecture.md
  • Sources: skills/nemotron-nano3/paper/evaluation.md; skills/nemotron-nano3/model-card.md

Better when needed:

  • Paper: skills/nemotron-nano3/paper/rl.md
  • Public recipe: skills/nemotron-nano3/recipes/stage2_rl.md

If you synthesize across sources, say so explicitly:

  • Synthesis from paper + recipe summary: ...

Progressive Disclosure

Do not dump the whole knowledge base unless asked.

Preferred sequence:

  1. INDEX.md
  2. Frontmatter summary and key facts from one chunk
  3. Small table or bullet answer
  4. Full chunk excerpt summary only if the user wants detail

When a question spans both “paper” and “how to run it,” answer in two blocks:

  1. Paper answer
  2. Public recipe / reproduction answer

Show full SKILL.md (348 more words)Show less

Cross-Skill Handoff

If the user wants to implement something, switch from knowledge to pipeline-building:

  • “build a Nano3 SFT pipeline”
  • “how do I run the RL recipe?”
  • “generate the commands/configs”
  • “customize this for my data”
  • “which steps should I chain?”

Then say:

“This is now a build/customization task. I should hand off to /nemotron-customize.”

Use skills/nemotron-nano3/context/quick-reference.md to map:

  • paper concept → public recipe stage
  • public recipe stage → nemotron-customize step or Explorer-mode fallback

Important caveat:

  • nemotron-customize currently has direct catalog support for packing, SFT, RL, eval, conversion, curation, translation
  • Stage 0 pretraining does not yet have a public catalog step in src/nemotron/steps/STEPS.md; route that as an Explorer-mode or direct recipe task

Calibration Examples

Architecture question

User:

How many parameters are active in Nemotron 3 Nano and why is it faster than similarly sized models?

Answer pattern:

  1. State the totals: 31.6B total, 3.2B active per forward pass, 3.6B including embeddings
  2. Explain sparse MoE + hybrid Mamba/Transformer design
  3. Cite paper/architecture.md
Reproduction question

User:

Can I reproduce the paper’s SFT and RL results with the public repo?

Answer pattern:

  1. Say not exactly
  2. Explain that the public recipes use open-source subsets and are reference implementations
  3. Point to stage summaries and recipes/overview.md
  4. If they want commands, hand off to /nemotron-customize
Benchmark question

User:

How does Nano3 compare to Qwen3 and GPT-OSS?

Answer pattern:

  1. Use paper/evaluation.md
  2. Separate base-model comparisons from post-trained comparisons
  3. Mention the throughput comparison and the long-context comparison
  4. Cite the file and, if needed, model-card.md

Boundaries

Do
  • Answer factual questions about Nano3
  • Cite the exact skill file(s) used
  • Distinguish paper results from repo recipes
  • Mention when the public recipe is only a partial/open-data reproduction
  • Hand off to /nemotron-customize when the task becomes procedural or generative
Don’t
  • Don’t generate new training code from this skill
  • Don’t invent missing hyperparameters or dataset sizes
  • Don’t claim the public repo exactly reproduces NVIDIA’s internal training/eval runs
  • Don’t treat model-card deployment snippets as benchmark methodology
  • Don’t speculate about unpublished data, internal infra, or unreleased steps

Quick Path Reference

text
skills/nemotron-nano3/
├── INDEX.md
├── model-card.md
├── paper/
│   ├── _overview.md
│   ├── architecture.md
│   ├── pretraining.md
│   ├── sft.md
│   ├── rl.md
│   ├── evaluation.md
│   ├── data.md
│   └── safety.md
├── recipes/
│   ├── overview.md
│   ├── stage0_pretrain.md
│   ├── stage1_sft.md
│   ├── stage2_rl.md
│   └── stage3_eval.md
└── context/
    ├── index.toml
    └── quick-reference.md

Use this skill to understand Nano3.
Use /nemotron-customize to build with Nano3.

© NVIDIA-NeMo, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 17 other files in skills/nemotron-nano3 of NVIDIA-NeMo/Nemotron.

  • SKILL.md
  • INDEX.md
  • context/index.toml
  • context/quick-reference.md
  • model-card.md
  • paper/_overview.md
  • paper/architecture.md
  • paper/data.md
  • paper/evaluation.md
  • paper/pretraining.md
  • paper/rl.md
  • paper/safety.md
  • paper/sft.md
  • recipes/overview.md
  • recipes/stage0_pretrain.md
  • recipes/stage1_sft.md
  • recipes/stage2_rl.md
  • recipes/stage3_eval.md

Open the folder on GitHubat commit ca8c409

Compare with similar skills

Nemotron Nano3 next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Nemotron Nano3 compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Nemotron Nano3 this skillNVIDIA-NeMo/Nemotron2.1k—~1.9kAutomated safety check: PassApache-2.0
Matlab Use Visual Inspectionmatlab/matlab-agentic-toolkit1.1k—~3.1kAutomated safety check: PassCustom licence
Model Serving Kubernetessickn33/agentic-awesome-skills47k1 repos~2.3kAutomated safety check: PassMIT
Rtvi Vlm Customize ModelNVIDIA/skills3.6k—~5kAutomated safety check: NotesApache-2.0
Model Serving KubernetesBagelHole/DevOps-Security-Agent-Skills1.2k—~2.1kAutomated safety check: PassMIT
SageMaker Serving Image Selectionhuggingface/skills11k1 repos~4.6kAutomated safety check: PassApache-2.0

Similar skills

  • Matlab Use Visual Inspection

    matlab/matlab-agentic-toolkit

    Build machine vision inspection systems with MATLAB Visual Inspection Toolbox.

    1.1k GitHub stars~3.1k tokensUpdated 2 days ago
    AI & LLM EngineeringAuto-check passed
  • Model Serving Kubernetes

    sickn33/agentic-awesome-skills

    Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server.

    47k GitHub starsUsed in 1 repo~2.3k tokens
    DevOps & CloudAuto-check passed
  • Official

    How to swap the VLM in the VSS Alerts Blueprint — covers RTVI-VLM microservice deployment methods, all three VLM consumers (rtvi-vlm, vlm-as-verifier, vss-agent), and health checks.

    3.6k GitHub stars~5k tokensUpdated yesterday
    Backend & APIsAuto-check: notes
  • Model Serving Kubernetes

    BagelHole/DevOps-Security-Agent-Skills

    Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server.

    1.2k GitHub stars~2.1k tokensUpdated 4 mo ago
    DevOps & CloudAuto-check passed
  • Official

    Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.

    11k GitHub starsUsed in 1 repo~4.6k tokens
    AI & LLM EngineeringAuto-check passed
  • SGLang Structured Serving

    Orchestra-Research/AI-Research-SKILLs

    Covers serving LLMs with SGLang, whose RadixAttention reuses cached prefixes, and constraining output to JSON, regex or grammar for agent and tool-calling workloads.

    13k GitHub starsUsed in 2 repos~2.9k tokens
    AI & LLM EngineeringAuto-check passed

More from NVIDIA-NeMo/Nemotron

All 10 skills in this repo
  • Nemotron Add Model

    NVIDIA-NeMo/Nemotron

    Onboard a new model family (Nemotron or third-party) into skills/ — paper chunks, recipe summaries, context packs, and model card.

    2.1k GitHub stars~2.2k tokensUpdated 4 days ago
    Auto-check passed
  • Nemotron Add Pattern

    NVIDIA-NeMo/Nemotron

    Add a cross-cutting decision pattern under src/nemotron/steps/patterns/.

    2.1k GitHub stars~1.4k tokensUpdated 4 days ago
    Auto-check passed
  • Nemotron Add Step

    NVIDIA-NeMo/Nemotron

    Add a new step under src/nemotron/steps/<category/<stepid/ — manifest (step.toml), runner glue, configs, and per-step README.md.

    2.1k GitHub stars~1.7k tokensUpdated 4 days ago
    Auto-check passed
  • Nemotron Customizer Airgap

    NVIDIA-NeMo/Nemotron

    Prepare, validate, build, and use Nemotron Customizer airgap image bundles for offline clusters.

    2.1k GitHub stars~1.2k tokensUpdated 4 days ago
    Auto-check passed
  • Nemotron Super3

    NVIDIA-NeMo/Nemotron

    Reference desk for NVIDIA Nemotron 3 Super — architecture, training data, recipes (pretrain/SFT/RL/eval/quantization), and deployment notes.

    2.1k GitHub stars~2.4k tokensUpdated 4 days ago
    Auto-check passed
  • Run the Nemotron-3.5 Lightning Text2SQL LoRA fine-tuning tutorial (NeMo Megatron-Bridge) end-to-end for the user on a single node: data prep, checkpoint conversion, LoRA fine-tuning of the 30B-A3B…

    2.1k GitHub stars~1.9k tokensUpdated 4 days ago
    Auto-check passed

Questions about Nemotron Nano3

What does Nemotron Nano3 do?

Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment. Nemotron Nano3 is an agent skill from NVIDIA-NeMo/Nemotron. Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment.

When should I use Nemotron Nano3?

Nemotron Nano3 fits situations like: the user asks facts about the model rather than building a pipeline; tasks that involve LLM inference and serving; tasks that involve Deployment.

How do I install Nemotron Nano3 in Claude Code?

Run `npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-nano3 -a claude-code`. Or copy the skill folder (skills/nemotron-nano3 in NVIDIA-NeMo/Nemotron) into .claude/skills/nemotron-nano3 in your project. Claude Code loads it when a task matches its description.

How do I install Nemotron Nano3 in Codex?

Run `npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-nano3 -a codex`. Or copy the skill folder (skills/nemotron-nano3 in NVIDIA-NeMo/Nemotron) into .agents/skills/nemotron-nano3 in your project. Codex loads it when a task matches its description.

Can I use Nemotron Nano3 in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-nano3 -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemotron-nano3, .gemini/skills/nemotron-nano3, .github/skills/nemotron-nano3 and .opencode/skills/nemotron-nano3 in your project.

What does Nemotron Nano3 need to run?

SKILL.md names no scripts, command-line tools or credentials: Nemotron Nano3 is instructions for the agent only.

Does Nemotron Nano3 access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nemotron Nano3 safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nemotron Nano3 use?

Nemotron Nano3 is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nemotron Nano3 use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nemotron Nano3?

Skills that share tags, products or a category with Nemotron Nano3: Matlab Use Visual Inspection (matlab/matlab-agentic-toolkit, 1.1k stars), Model Serving Kubernetes (sickn33/agentic-awesome-skills, 47k stars), Rtvi Vlm Customize Model (NVIDIA/skills, 3.6k stars) and Model Serving Kubernetes (BagelHole/DevOps-Security-Agent-Skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nemotron Nano3?

NVIDIA-NeMo (a GitHub organization) maintains it in NVIDIA-NeMo/Nemotron, which has 2,142 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 6, 2026.

Source: NVIDIA-NeMo/Nemotron on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.