Optimum

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/.
• export (bool, defaults to False) — Defines whether the provided model_id needs to be exported to the targeted format.
• force_download (bool, 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 (Optional[Union[bool,str]], defaults to None) — Deprecated. Please use the token argument instead.
• token (Optional[Union[bool,str]], defaults to None) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running huggingface-cli login (stored in huggingface_hub.constants.HF_TOKEN_PATH).
• cache_dir (Optional[str], 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.
• subfolder (str, 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.
• config (Optional[transformers.PretrainedConfig], defaults to None) — The model configuration.
• local_files_only (Optional[bool], defaults to False) — Whether or not to only look at local files (i.e., do not try to download the model).
• trust_remote_code (bool, defaults to False) — Whether or not to allow for custom code defined on the Hub in their own modeling. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
• revision (Optional[str], defaults to None) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
• provider (str, defaults to "VitisAIExecutionProvider") — ONNX Runtime provider to use for loading the model. See https://onnxruntime.ai/docs/execution-providers/ for possible providers.
• session_options (Optional[onnxruntime.SessionOptions], defaults to None), — ONNX Runtime session options to use for loading the model.
• provider_options (Optional[Dict[str, Any]], defaults to None) — Provider option dictionaries corresponding to the provider used. See available options for each provider: https://onnxruntime.ai/docs/api/c/group___global.html .
• kwargs (Dict[str, Any]) — Will be passed to the underlying model loading methods.

Parameters for decoder models (RyzenAIForSpeechSeq2Seq)

• use_cache (Optional[bool], defaults to True) — Whether or not past key/values cache should be used. Defaults to True.

The loaded RyzenAIModel model.

Parameters

• save_directory (Union[str, 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.

Parameters

• model_path (Union[str, Path]) — Path to the model.
• input_shape_dict (Dict[str, Tuple[int]]) — Input shapes for the model.
• output_shape_dict (Dict[str, Tuple[int]]) — Output shapes for the model.

Path to the model after updating the input shapes.

• ValueError — If the model provided has dynamic axes in input/output and no input/output shape is provided.