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Upload a trained adapter to Hugging Face Hub in the intrinsic directory layout. Creates or updates a private Hugging Face repository and uploads adapter weights into a <intrinsic_name>/<base_model>/<adapter_type> sub-directory, together with the required io.yaml configuration file. If an INTRINSIC_README.md exists in the weight directory it is also uploaded as the repository’s root README.md. Requires an authenticated Hugging Face token obtained via huggingface-cli login.

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Functions

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FUNC upload_intrinsic 

upload_intrinsic(weight_path: str, model_name: str, base_model: str, type: Literal['lora', 'alora'], io_yaml: str, private: bool = True)
Upload an adapter to Hugging Face Hub using the intrinsic directory layout. Creates or updates a private Hugging Face repository and uploads adapter weights into a <intrinsic_name>/<base_model>/<adapter_type> sub-directory, together with the io.yaml configuration file. If an INTRINSIC_README.md exists in the weight directory it is also uploaded as the repository root README.md. Args:
  • weight_path: Local directory containing the adapter weights (output of save_pretrained).
  • model_name: Target Hugging Face repository name in "<userid>/<intrinsic_name>" format (e.g. "acme/carbchecker-alora").
  • base_model: Base model ID or path (e.g. "ibm-granite/granite-3.3-2b-instruct"). Must contain at most one "/" separator.
  • type: Adapter type, used as the leaf directory name in the repository layout.
  • io_yaml: Path to the io.yaml configuration file for intrinsic input/output processing.
  • private: Whether the repository should be private. Currently only True is supported.
Raises:
  • AssertionError: If weight_path or io_yaml do not exist, if private is False, if base_model contains more than one "/" separator, or if model_name does not contain exactly one "/" separator.
  • OSError: If no Hugging Face authentication token is found.