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LLM-assisted generator for adapter intrinsic README files. Uses a MelleaSession with rejection sampling to derive README template variables from a JSONL training dataset — including a high-level description, the inferred Python argument list, and Jinja2-renderable sample rows. Validates the generated output with deterministic requirements (correct naming conventions, syntactically valid argument lists) before rendering the final INTRINSIC_README.md via a Jinja2 template.

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Functions

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FUNC make_readme_jinja_dict 

make_readme_jinja_dict(m: MelleaSession, dataset_path: str, base_model: str, prompt_file: str, name: str, hints: str | None) -> dict[str, Any]
Generate all template variables for the intrinsic README using an LLM. Loads the first five lines of the JSONL dataset, determines the input structure, and uses m.instruct with deterministic requirements and rejection sampling to generate README template variables. Args:
  • m: Active MelleaSession to use for LLM generation.
  • dataset_path: Path to the JSONL training dataset file.
  • base_model: Base model ID or path used to train the adapter.
  • prompt_file: Path to the prompt format file (empty string if not provided).
  • name: Destination model name on Hugging Face Hub (e.g. "acme/carbchecker-alora").
  • hints: Optional string of additional domain hints to include in the prompt.
Returns:
  • Dict of Jinja2 template variables for rendering the INTRINSIC_README.md.

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FUNC generate_readme 

generate_readme(dataset_path: str, base_model: str, prompt_file: str | None, output_path: str, name: str, hints: str | None) -> str
Generate an INTRINSIC_README.md file from the dataset and template. Creates a MelleaSession, uses the LLM to generate template variables, renders the Jinja template, and writes the result to output_path. Args:
  • dataset_path: Path to the JSONL training dataset file.
  • base_model: Base model ID or path used to train the adapter.
  • prompt_file: Path to the prompt format file, or None.
  • output_path: Destination path for the generated README file.
  • name: Destination model name on Hugging Face Hub.
  • hints: Optional string of additional domain hints for the LLM.
Returns:
  • The path to the written output file (same as output_path).

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Classes

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CLASS ReadmeTemplateVars

Pydantic model holding all variables required to render the intrinsic README template. Attributes:
  • high_level_description: A 2-3 sentence description of what the intrinsic adapter does.
  • dataset_description: Brief description of the training dataset contents and format.
  • userid: HuggingFace user ID (the namespace portion of the model name).
  • intrinsic_name: Short snake_case identifier for the intrinsic (e.g. "carbchecker").
  • intrinsic_name_camelcase: CamelCase version of intrinsic_name (e.g. "CarbChecker").
  • arglist: Python function argument list with type hints (e.g. "description\: str").
  • arglist_without_type_annotations: Argument list without type hints (e.g. "description").