# nemo-automodel-recipe-development

Guides the agent in creating and editing NeMo AutoModel training and evaluation recipes. It covers YAML configuration, builder functions, and CLI execution flow.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1900 installs · ★ 3735
- Author: NVIDIA
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-automodel-recipe-development/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/nemo-automodel-recipe-development

## What it does

The skill provides rules for generating NeMo AutoModel training and fine-tuning configs. It maps configuration sections to Python callables using the `_target_` pattern. For full-parameter training with AdamW, it enforces setting `model.dtype: float32` to maintain fp32 master weights.

Recipe architecture relies on `BaseRecipe` subclasses. Builders like `build_model()` and `build_optimizer()` construct individual training components. The skill defines a strict execution order: PEFT and FP8 run before QAT, checkpoint restoration, parameter freezing, and sharding via FSDP2, Megatron-FSDP, or DDP.

It maintains a strict operational boundary. The instructions exclude cluster launcher configuration and standalone distributed strategy decisions unless configured directly inside a recipe YAML. Focus stays locked on YAML keys, builder functions, and verification with `automodel <config.yaml>`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-automodel-recipe-development -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/nemo-automodel-recipe-development/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/nemo-automodel-recipe-development/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-automodel-recipe-development -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/nemo-automodel-recipe-development/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-automodel-recipe-development -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/nemo-automodel-recipe-development/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills nemo-automodel-recipe-development
```

2. **Manually: put the files in place**
- File: `.github/skills/nemo-automodel-recipe-development/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-automodel-recipe-development -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/nemo-automodel-recipe-development
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/nemo-automodel-recipe-development/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill nemo-automodel-recipe-development -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-automodel-recipe-development/SKILL.md

## When to use it
- write YAML recipes for NeMo AutoModel fine-tuning
- configure validation cadences and checkpoint intervals
- set up optimizer and dataset parameters using `_target_`
- verify component ordering for PEFT, FP8, and sharding

## Example prompts
- Create a NeMo AutoModel finetuning recipe config for Llama 3.2.
- How do I set val_check_interval and checkpoint_interval in the YAML config?
- Show me how to override optimizer.lr from the automodel CLI.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 60504459231fc94c.

BEGIN THIRD-PARTY FILE 60504459231fc94c
```text
---
name: nemo-automodel-recipe-development
description: Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.
when_to_use: Creating or modifying training, SFT, or eval recipes, adding new YAML config fields, debugging recipe construction or trainer issues, or understanding the recipe execution flow.
license: Apache-2.0
metadata:
  author: NVIDIA
  tags:
    - nemo-automodel
    - recipe-development
---

# NeMo AutoModel Recipe Development
<!-- NVSkills signature refresh requested for AM-519. -->

## Instructions

For recipe questions, answer with the smallest complete path to action:

1. Name the relevant recipe file or YAML section.
2. List the builder functions or config keys involved.
3. Include a minimal YAML or command example when the question asks how to
   configure something.
4. End with a local validation command or tiny CPU-compatible test.

For conceptual recipe questions, answer from this skill without inspecting the
repository or loading other AutoModel skills unless the user asks you to edit
files. Keep the response focused on recipe YAML, builders, CLI routing, tests,
and local validation.

Use these compact answer patterns for common questions:

- New finetuning recipe variant: start from the closest file under
  `nemo_automodel/recipes/`, update the model, dataset or dataloader,
  optimizer, loss, LR scheduler, step scheduler, and checkpoint builders,
  register a recipe alias only if adding a new recipe class, add example
  YAML under `examples/`, then add a tiny CPU-compatible unit test and run
  `automodel <config.yaml>`.
- `_target_` fields: describe `_target_` as the fully qualified Python callable,
  explain that sibling keys become keyword arguments, show optimizer and dataset
  examples, and mention nested CLI overrides such as `--optimizer.lr`.
- Validation and checkpointing: name `step_scheduler.val_check_interval`,
  `step_scheduler.checkpoint_interval`, `validation_dataset`,
  `restore_from.path`, and consolidated safetensors; include the minimal YAML
  snippet from this skill.

For validation and checkpointing, always name:

- `step_scheduler.val_check_interval` for validation cadence.
- `step_scheduler.checkpoint_interval` for save cadence.
- `validation_dataset` as the validation dataloader source.
- `restore_from.path` for resume.
- Consolidated safetensors as the default checkpoint format for HF ecosystem
  compatibility.

## Routing Boundary

Use this skill for recipe construction and execution-flow questions: YAML
```
END THIRD-PARTY FILE 60504459231fc94c

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First 2563 of 11931 bytes. © NVIDIA · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-automodel-recipe-development/SKILL.md

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