nemo-automodel-recipe-development

Solid

Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.

AI & Automation 4 stars 0 forks Updated today Apache-2.0

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Quality Score: 83/100

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100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# NeMo AutoModel Recipe Development ## 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 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`. - Validation and checkpointing: name `step_scheduler.val_check_interval`, `step_scheduler.checkpoint_interval`, `validation_dataset`, `rest...

Details

Author
yangwhale
Repository
yangwhale/CloseCrab
Created
6 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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