nemotron-nano3
Provides technical documentation and model-card facts for Nemotron 3 Nano series.
Install
mkdir -p .claude/skills/nemotron-nano3 && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/11443" && unzip -o skill.zip -d .claude/skills/nemotron-nano3 && rm skill.zipInstalls to .claude/skills/nemotron-nano3
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Answer factual questions about Nemotron 3 Nano architecture
- →Provide information on training data and recipes
- →Detail evaluation benchmarks and quantization
- →Explain deployment behavior of the model
- →Distinguish between paper claims and public recipe implementations
How it works
The skill retrieves information from a structured knowledge base, prioritizing paper chunks, recipe summaries, model cards, and repo documentation to answer factual questions about Nemotron 3 Nano.
Inputs & outputs
When to use nemotron-nano3
- →Look up model facts
- →Check nemotron training data
- →Find evaluation benchmarks
About this skill
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:
- Paper chunks — the technical report split into question-friendly sections
- Recipe summaries — how the public
src/nemotron/recipes/nano3/code maps to the paper - Model card — released checkpoints, deployment, license, safety, intended use
- 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:
skills/nemotron-nano3/paper/*.mdskills/nemotron-nano3/recipes/*.mdskills/nemotron-nano3/model-card.mddocs/nemotron/nano3/*.mdandsrc/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:
skills/nemotron-nano3/INDEX.md- Matching file frontmatter summary in:
skills/nemotron-nano3/paper/*.mdskills/nemotron-nano3/recipes/*.md
- 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 type | Read first |
|---|---|
| “What is Nano3?” | model-card.md, paper/_overview.md |
| Architecture / active params / context length | paper/architecture.md |
| Pretraining corpus / schedule / scaling | paper/data.md, paper/pretraining.md |
| SFT data / chat template / reasoning control | paper/sft.md |
| RLVR / RLHF / GRPO / DPO | paper/rl.md, paper/safety.md |
| Benchmark numbers / comparisons | paper/evaluation.md, model-card.md |
| Safety / refusal / over-refusal / hallucinated tools | paper/safety.md, model-card.md |
| Public recipe mapping | recipes/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.mdSources: skills/nemotron-nano3/paper/evaluation.md; skills/nemotron-nano3/model-card.md
Better when needed:
Paper: skills/nemotron-nano3/paper/rl.mdPublic 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:
INDEX.md- Frontmatter summary and key facts from one chunk
- Small table or bullet answer
- 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:
- Paper answer
- 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-customizestep or Explorer-mode fallback
Important caveat:
nemotron-customizecurrently 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:
- State the totals: 31.6B total, 3.2B active per forward pass, 3.6B including embeddings
- Explain sparse MoE + hybrid Mamba/Transformer design
- Cite
paper/architecture.md
Reproduction question
User:
Can I reproduce the paper’s SFT and RL results with the public repo?
Answer pattern:
- Say not exactly
- Explain that the public recipes use open-source subsets and are reference implementations
- Point to stage summaries and
recipes/overview.md - If they want commands, hand off to
/nemotron-customize
Benchmark question
User:
How does Nano3 compare to Qwen3 and GPT-OSS?
Answer pattern:
- Use
paper/evaluation.md - Separate base-model comparisons from post-trained comparisons
- Mention the throughput comparison and the long-context comparison
- 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-customizewhen 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
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.
When not to use it
- →When the user wants to build a pipeline
- →When the user wants to fine-tune the model
- →When the user wants to generate pipeline code
Limitations
- →Does not generate new training code
- →Does not invent missing hyperparameters or dataset sizes
- →Does not speculate about unpublished data
How it compares
This skill acts as a dedicated reference desk for Nemotron 3 Nano facts, providing source-grounded answers without generating code or pipelines, unlike a general-purpose code generation tool.
Compared to similar skills
nemotron-nano3 side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| nemotron-nano3 (this skill) | 0 | 2mo | No flags | Intermediate |
| esm | 3 | 7mo | Review | Advanced |
| hugging-face-paper-publisher | 6 | 6mo | Review | Intermediate |
| torchdrug | 3 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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