Agent skill · software engineering · nvidia
nemo-automodel-recipe-development
Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.
Why this skill is useful
Provides specific YAML structure and builder function patterns for NeMo AutoModel recipes that the AI wouldn't reliably generate on its own.
What it needs
Requires nemo installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
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 nemoautomodel/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 stepscheduler.valcheckinterval, stepscheduler.checkpointinterval, validationdataset, restorefrom.path, and consolidated safetensors; include the minimal YAML snippet from this skill. For validation and checkpointing, always name: stepscheduler.valcheckinterval for validation cadence. stepscheduler.checkpointinterval for save cadence. validationdataset as the validation dataloader source. restorefrom.path for resume. Consolidated safetensors as the default checkpoint format for HF ecosystem compatibility. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/nemo-automodel-recipe-development