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Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.
Passed 21 of 22 test cases with the skill, and 7 of 22 without it.
and 20 more in the full report
15 fixed6 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
In NeMo AutoModel recipe execution, components like PEFT, quantization, parameter freezing, sharding, and torch.compile are applied in a strict sequence after builder functions run. If an engineer wants to configure FSDP2 sharding and LoRA, which one gets applied first during execution setup?
| Checked | Without skill | With skill |
|---|---|---|
| Applies PEFT before parameter freezing and sharding | Passed | Passed |
| Applies FP8 quantization before checkpoint restoration | Missed | Passed |
| Applies sharding before device placement and torch.compile | Missed | Passed |
The instructions your model reads when the skill loads.
For recipe questions, answer with the smallest complete path to action:
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:
nemo_automodel/recipes/, update the model, dataset or dataloader,
optimizer, loss, LR scheduler, step scheduler, and checkpoint builders,
register a CLI route only if adding a command or domain alias, add example
YAML under examples/, then add a tiny CPU-compatible unit test and run
automodel finetune llm -c <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.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.test cases passed, vs 7 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.