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name: package-apis-configuration description: "Inspect and use LLM Foundry package APIs, registries, MPT/HF model classes, tokenizers, callbacks, optimizers, metrics, loggers, config transforms, optional backends, and MCLI adaptation patterns." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Package APIs and Configuration

Use this sub-skill when the task is about LLM Foundry's installed Python package surface: package import health, registry entries, custom registry extensions, MPT/Hugging Face model constructors, tokenizers, callbacks, optimizers, schedulers, metrics, loggers, configuration transforms, optional dependency gates, or MosaicML platform YAML adaptation.

Start here:

• Use references/api-reference.md for package identity, installed registry entries, public signatures, and builder contracts.
• Use references/model-configuration.md for MPTConfig, MPTForCausalLM, ComposerMPTCausalLM, ComposerHFCausalLM, and ComposerHFT5 configuration rules.
• Use references/registry-and-extension.md for llmfoundry registry, Python entry points, code_paths, import_file, and construct_from_registry patterns.
• Use references/mcli-platform.md for adapting LLM Foundry YAMLs to MosaicML platform/MCLI jobs.
• Use references/troubleshooting.md when imports, registry construction, optional packages, CUDA-only components, credentials, or HF remote-code loads fail.
• Run scripts/llmfoundry_api_probe.py before deeper package work to print package version, registry entries, a tiny MPTConfig check, CUDA availability, and optional dependency status without downloads or training.

Operating rules:

1. Prefer registry and signature inspection before instantiating models. build_tokenizer and Hugging Face model wrappers can download remote assets; do not call them unless the task explicitly allows network/model access.
2. For CPU-only or quick API checks, override MPT attention to attn_impl: torch and ComposerMPT loss to loss_fn: torch_crossentropy; defaults favor GPU/flash-attention paths.
3. Treat registry names as exact keys. If a key is unknown, first confirm the package import completed, then check llmfoundry registry get <group> or direct registry.<group>.get_all().
4. For custom code, prefer a bounded code_paths import file for one run and Python entry points for reusable packages. The imported code must register into the appropriate llmfoundry.registry object before builders run.
5. Preserve constructor keyword boundaries: remove name before passing kwargs to constructors, do not pass optimizer params through YAML, and only pass callback train_config through build_callback's train_config argument.
6. Route full data preparation, training, evaluation, generation, and checkpoint conversion workflows to their owning sub-skills. This sub-skill only covers package APIs/configuration and platform adaptation surfaces.
7. Exclude maintenance-only CI, lint, release, and packaging-policy tasks unless they directly affect runtime package import or registry behavior.