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Nemotron Nano3

OrganizationPopular
NVIDIA-NeMo
nemotron-nano3

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.

Overview

PublisherNVIDIA-NeMo
RepositoryNemotron
Skill namenemotron-nano3
Stars
2.1K
Forks
419
Bundled files
17
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 17 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NVIDIA-NeMo on GitHub. Read the source before you install it.

Installation

Install the Nemotron Nano3 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVIDIA-NeMo/Nemotron.git /tmp/Nemotron
mkdir -p .claude/skills
cp -r /tmp/Nemotron/skills/nemotron-nano3 .claude/skills/nemotron-nano3
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nemotron Nano3 in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nemotron Nano3 on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nemotron Nano3 is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nemotron Nano3 AI skill do?

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.

Why use Nemotron Nano3 on TypingMind?

Because you install it once and use it with any model. Nemotron Nano3 is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nemotron Nano3 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-NeMo/Nemotron/tree/main/skills/nemotron-nano3. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nemotron Nano3?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nemotron Nano3?

As many as you like. As long as a model supports skills, you can use Nemotron Nano3 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nemotron Nano3 AI skill free?

Yes. It is published on GitHub by NVIDIA-NeMo under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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