Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

Mixins & serialization methods

Mixins

The huggingface_hub library offers a range of mixins that can be used as a parent class for your objects, in order to provide simple uploading and downloading functions.

PyTorch

class huggingface_hub.ModelHubMixin

< >

( )

A Generic Base Model Hub Mixin. Define your own mixin for anything by inheriting from this class and overwriting _from_pretrained and _save_pretrained to define custom logic for saving/loading your classes. See huggingface_hub.PyTorchModelHubMixin for an example.

from_pretrained

< >

( pretrained_model_name_or_path: str force_download: bool = False resume_download: bool = False proxies: typing.Dict = None use_auth_token: typing.Optional[str] = None cache_dir: typing.Optional[str] = None local_files_only: bool = False **model_kwargs )

Parameters

  • pretrained_model_name_or_path (str or os.PathLike) — 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.
    • You can add revision by appending @ at the end of model_id simply like this: dbmdz/bert-base-german-cased@main Revision is 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.
    • A path to a directory containing model weights saved using save_pretrained, e.g., ./my_model_directory/.
    • None if you are both providing the configuration and state dictionary (resp. with keyword arguments config and state_dict).
  • force_download (bool, optional, defaults to False) — Whether to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
  • resume_download (bool, optional, defaults to False) — Whether to delete incompletely received files. Will attempt to resume the download if such a file exists.
  • proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.
  • use_auth_token (str or bool, optional) — 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 (Union[str, os.PathLike], optional) — 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 to only look at local files (i.e., do not try to download the model).
  • model_kwargs (Dict, optional) — model_kwargs will be passed to the model during initialization

Instantiate a pretrained PyTorch model from a pre-trained model configuration from huggingface-hub. The model is set in evaluation mode by default using model.eval() (Dropout modules are deactivated). To train the model, you should first set it back in training mode with model.train().

Passing use_auth_token=True is required when you want to use a private model.

push_to_hub

< >

( repo_path_or_name: typing.Optional[str] = None repo_url: typing.Optional[str] = None commit_message: typing.Optional[str] = 'Add model' organization: typing.Optional[str] = None private: typing.Optional[bool] = None api_endpoint: typing.Optional[str] = None use_auth_token: typing.Union[bool, str, NoneType] = None git_user: typing.Optional[str] = None git_email: typing.Optional[str] = None config: typing.Optional[dict] = None skip_lfs_files: bool = False )

Parameters

  • repo_path_or_name (str, optional) — Can either be a repository name for your model or tokenizer in the Hub or a path to a local folder (in which case the repository will have the name of that local folder). If not specified, will default to the name given by repo_url and a local directory with that name will be created.
  • repo_url (str, optional) — Specify this in case you want to push to an existing repository in the hub. If unspecified, a new repository will be created in your namespace (unless you specify an organization) with repo_name.
  • commit_message (str, optional) — Message to commit while pushing. Will default to "add config", "add tokenizer" or "add model" depending on the type of the class.
  • organization (str, optional) — Organization in which you want to push your model or tokenizer (you must be a member of this organization).
  • private (bool, optional) — Whether the repository created should be private.
  • api_endpoint (str, optional) — The API endpoint to use when pushing the model to the hub.
  • use_auth_token (bool or str, optional) — 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). Will default to True if repo_url is not specified.
  • git_user (str, optional) — will override the git config user.name for committing and pushing files to the hub.
  • git_email (str, optional) — will override the git config user.email for committing and pushing files to the hub.
  • config (dict, optional) — Configuration object to be saved alongside the model weights.
  • skip_lfs_files (bool, optional, defaults to False) — Whether to skip git-LFS files or not.

Upload model checkpoint or tokenizer files to the Hub while synchronizing a local clone of the repo in repo_path_or_name.

save_pretrained

< >

( save_directory: str config: typing.Optional[dict] = None push_to_hub: bool = False **kwargs )

Parameters

  • save_directory (str) — Specify directory in which you want to save weights.
  • config (dict, optional) — specify config (must be dict) in case you want to save it.
  • push_to_hub (bool, optional, defaults to False) — Set it to True in case you want to push your weights to huggingface_hub
  • kwargs (Dict, optional) — kwargs will be passed to push_to_hub

Save weights in local directory.

Keras

huggingface_hub.from_pretrained_keras

< >

( *args **kwargs )

Parameters

  • pretrained_model_name_or_path (str or os.PathLike) — 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.
    • You can add revision by appending @ at the end of model_id simply like this: dbmdz/bert-base-german-cased@main Revision is 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.
    • A path to a directory containing model weights saved using save_pretrained, e.g., ./my_model_directory/.
    • None if you are both providing the configuration and state dictionary (resp. with keyword arguments config and state_dict).
  • force_download (bool, optional, defaults to False) — Whether to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
  • resume_download (bool, optional, defaults to False) — Whether to delete incompletely received files. Will attempt to resume the download if such a file exists.
  • proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.
  • use_auth_token (str or bool, optional) — 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 (Union[str, os.PathLike], optional) — 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 to only look at local files (i.e., do not try to download the model).
  • model_kwargs (Dict, optional) — model_kwargs will be passed to the model during initialization

Instantiate a pretrained Keras model from a pre-trained model from the Hub. The model is expected to be in SavedModel format.```

Passing use_auth_token=True is required when you want to use a private model.

huggingface_hub.push_to_hub_keras

< >

( model repo_path_or_name: typing.Optional[str] = None repo_url: typing.Optional[str] = None log_dir: typing.Optional[str] = None commit_message: typing.Optional[str] = 'Add model' organization: typing.Optional[str] = None private: typing.Optional[bool] = None api_endpoint: typing.Optional[str] = None use_auth_token: typing.Union[bool, str, NoneType] = True git_user: typing.Optional[str] = None git_email: typing.Optional[str] = None config: typing.Optional[dict] = None include_optimizer: typing.Optional[bool] = False tags: typing.Union[list, str, NoneType] = None plot_model: typing.Optional[bool] = True **model_save_kwargs )

Parameters

  • model (Keras.Model) — The Keras model you’d like to push to the Hub. The model must be compiled and built.
  • repo_path_or_name (str, optional) — Can either be a repository name for your model or tokenizer in the Hub or a path to a local folder (in which case the repository will have the name of that local folder). If not specified, will default to the name given by repo_url and a local directory with that name will be created.
  • repo_url (str, optional) — Specify this in case you want to push to an existing repository in the Hub. If unspecified, a new repository will be created in your namespace (unless you specify an organization) with repo_name.
  • log_dir (str, optional) — TensorBoard logging directory to be pushed. The Hub automatically hosts and displays a TensorBoard instance if log files are included in the repository.
  • commit_message (str, optional, defaults to “Add message”) — Message to commit while pushing.
  • organization (str, optional) — Organization in which you want to push your model or tokenizer (you must be a member of this organization).
  • private (bool, optional) — Whether the repository created should be private.
  • api_endpoint (str, optional) — The API endpoint to use when pushing the model to the hub.
  • use_auth_token (bool or str, optional, defaults to True) — 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). Will default to True.
  • git_user (str, optional) — will override the git config user.name for committing and pushing files to the Hub.
  • git_email (str, optional) — will override the git config user.email for committing and pushing files to the Hub.
  • config (dict, optional) — Configuration object to be saved alongside the model weights.
  • include_optimizer (bool, optional, defaults to False) — Whether or not to include optimizer during serialization.
  • tags (Union[list, str], optional) — List of tags that are related to model or string of a single tag. See example tags here.
  • plot_model (bool, optional, defaults to True) — Setting this to True will plot the model and put it in the model card. Requires graphviz and pydot to be installed.
  • model_save_kwargs(dict, optional) — model_save_kwargs will be passed to tf.keras.models.save_model().

Upload model checkpoint or tokenizer files to the Hub while synchronizing a local clone of the repo in repo_path_or_name.

huggingface_hub.save_pretrained_keras

< >

( model save_directory: str config: typing.Union[typing.Dict[str, typing.Any], NoneType] = None include_optimizer: typing.Optional[bool] = False plot_model: typing.Optional[bool] = True tags: typing.Union[list, str, NoneType] = None **model_save_kwargs )

Parameters

  • model (Keras.Model) — The Keras model you’d like to save. The model must be compiled and built.
  • save_directory (str) — Specify directory in which you want to save the Keras model.
  • config (dict, optional) — Configuration object to be saved alongside the model weights.
  • include_optimizer(bool, optional, defaults to False) — Whether or not to include optimizer in serialization.
  • plot_model (bool, optional, defaults to True) — Setting this to True will plot the model and put it in the model card. Requires graphviz and pydot to be installed.
  • tags (Union[str,list], optional) — List of tags that are related to model or string of a single tag. See example tags here.
  • model_save_kwargs(dict, optional) — model_save_kwargs will be passed to tf.keras.models.save_model().

Saves a Keras model to save_directory in SavedModel format. Use this if you’re using the Functional or Sequential APIs.

class huggingface_hub.KerasModelHubMixin

< >

( *args **kwargs )

Mixin to provide model Hub upload/download capabilities to Keras models. Override this class to obtain the following internal methods:

  • _from_pretrained, to load a model from the Hub or from local files.
  • _save_pretrained, to save a model in the SavedModel format.

Fastai

huggingface_hub.from_pretrained_fastai

< >

( repo_id: str revision: typing.Optional[str] = None )

Parameters

  • repo_id (str) — The location where the pickled fastai.Learner is. It can be either of the two:
    • Hosted on the Hugging Face Hub. E.g.: ‘espejelomar/fatai-pet-breeds-classification’ or ‘distilgpt2’. You can add a revision by appending @ at the end of repo_id. E.g.: dbmdz/bert-base-german-cased@main. Revision is the specific model version to use. Since we use a git-based system for storing models and other artifacts on the Hugging Face Hub, it can be a branch name, a tag name, or a commit id.
    • Hosted locally. repo_id would be a directory containing the pickle and a pyproject.toml indicating the fastai and fastcore versions used to build the fastai.Learner. E.g.: ./my_model_directory/.
  • revision (str, optional) — Revision at which the repo’s files are downloaded. See documentation of snapshot_download.

Load pretrained fastai model from the Hub or from a local directory.

huggingface_hub.push_to_hub_fastai

< >

( learner repo_id: str commit_message: typing.Optional[str] = 'Add model' private: typing.Optional[bool] = None token: typing.Optional[str] = None config: typing.Optional[dict] = None **kwargs )

Parameters

  • learner (Learner) — The *fastai.Learner’ you’d like to push to the Hub.
  • repo_id (str) — The repository id for your model in Hub in the format of “namespace/repo_name”. The namespace can be your individual account or an organization to which you have write access (for example, ‘stanfordnlp/stanza-de’).
  • commit_message (str`, optional*) — Message to commit while pushing. Will default to "add model".
  • private (bool, optional) — Whether or not the repository created should be private.
  • token (str, optional) — The Hugging Face account token to use as HTTP bearer authorization for remote files. If None, the token will be asked by a prompt.
  • config (dict, optional) — Configuration object to be saved alongside the model weights.

Upload learner checkpoint files to the Hub while synchronizing a local clone of the repo in repo_id.

Keyword Args: api_endpoint (str, optional): The API endpoint to use when pushing the model to the hub. git_user (str, optional): Will override the git config user.name for committing and pushing files to the hub. git_email (str, optional): Will override the git config user.email for committing and pushing files to the hub.

Raises the following error:

  • ValueError if the user is not log on to the Hugging Face Hub.