Parameters

• model_id (Union[str, Path]) — Can be either:
  • A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
  • A path to a directory containing a model saved using ~OptimizedModel.save_pretrained, e.g., ./my_model_directory/.
• from_transformers (bool, optional, defaults to False) — Defines whether the provided model_id contains a vanilla Transformers checkpoint.
• force_download (bool, optional, defaults to True) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
• use_auth_token (str, optional, defaults to None) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running transformers-cli login (stored in ~/.huggingface).
• cache_dir (str, optional, defaults to None) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
• local_files_only(bool, optional, defaults to False) — Whether or not to only look at local files (i.e., do not try to download the model).
• subfolder (str, optional, defaults to "") — In case the relevant files are located inside a subfolder of the model repo either locally or on huggingface.co, you can specify the folder name here.

The loaded optimized model.

Parameters

• save_directory (str or os.PathLike) — Directory to which to save. Will be created if it doesn’t exist.
• push_to_hub (bool, optional, defaults to False) — Whether or not to push your model to the Hugging Face model hub after saving it.

  Using push_to_hub=True will synchronize the repository you are pushing to with save_directory, which requires save_directory to be a local clone of the repo you are pushing to if it’s an existing folder. Pass along temp_dir=True to use a temporary directory instead.