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.

Generic

class huggingface_hub.ModelHubMixin

< source >

( )

A generic Hub mixin for machine learning models. Define your own mixin for any framework by inheriting from this class and overwriting the _from_pretrained and _save_pretrained methods to define custom logic for saving and loading your classes. See PyTorchModelHubMixin for an example.

_save_pretrained

< source >

( save_directory: typing.Union[str, pathlib.Path] )

Overwrite this method in subclass to define how to save your model.

_from_pretrained

< source >

( model_id revision cache_dir force_download proxies resume_download local_files_only token **model_kwargs )

Overwrite this method in subclass to define how to load your model from pretrained

from_pretrained

< source >

( pretrained_model_name_or_path: str force_download: bool = False resume_download: bool = False proxies: typing.Optional[typing.Dict] = None token: typing.Union[str, bool, NoneType] = 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.
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

Download and instantiate a model from the Hugging Face Hub.

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

push_to_hub

< source >

( repo_path_or_name: typing.Optional[str] = None repo_url: typing.Optional[str] = None commit_message: str = 'Add model' organization: typing.Optional[str] = None private: bool = False api_endpoint: typing.Optional[str] = None token: typing.Optional[str] = None git_user: typing.Optional[str] = None git_email: typing.Optional[str] = None config: typing.Optional[dict] = None skip_lfs_files: bool = False repo_id: typing.Optional[str] = None branch: typing.Optional[str] = None create_pr: typing.Optional[bool] = None allow_patterns: typing.Union[typing.List[str], str, NoneType] = None ignore_patterns: typing.Union[typing.List[str], str, NoneType] = None )

Parameters

repo_id (str, optional) — Repository name to which push.
commit_message (str, optional) — Message to commit while pushing.
private (bool, optional, defaults to False) — Whether the repository created should be private.
api_endpoint (str, optional) — The API endpoint to use when pushing the model to the hub.
token (str, optional) — The token to use as HTTP bearer authorization for remote files. If not set, will use the token set when logging in with transformers-cli login (stored in ~/.huggingface).
branch (str, optional) — The git branch on which to push the model. This defaults to the default branch as specified in your repository, which defaults to "main".
create_pr (boolean, optional) — Whether or not to create a Pull Request from branch with that commit. Defaults to False.
config (dict, optional) — Configuration object to be saved alongside the model weights.
allow_patterns (List[str] or str, optional) — If provided, only files matching at least one pattern are pushed.
ignore_patterns (List[str] or str, optional) — If provided, files matching any of the patterns are not pushed.

Upload model checkpoint to the Hub.

Use allow_patterns and ignore_patterns to precisely filter which files should be pushed to the hub. See upload_folder() reference for more details.

save_pretrained

< source >

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

Parameters

save_directory (str or Path) — 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) — Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the repository you want to push to with repo_id (will default to the name of save_directory in your namespace). kwargs — Additional key word arguments passed along to the ~utils.PushToHubMixin.push_to_hub method.

Save weights in local directory.

PyTorch

class huggingface_hub.PyTorchModelHubMixin

< source >

( )

Implementation of ModelHubMixin to provide model Hub upload/download capabilities to PyTorch models. 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().

Example:

>>> import torch
>>> import torch.nn as nn
>>> from huggingface_hub import PyTorchModelHubMixin


>>> class MyModel(nn.Module, PyTorchModelHubMixin):
...     def __init__(self):
...         super().__init__()
...         self.param = nn.Parameter(torch.rand(3, 4))
...         self.linear = nn.Linear(4, 5)

...     def forward(self, x):
...         return self.linear(x + self.param)


>>> model = MyModel()
>>> # Save model weights to local directory
>>> model.save_pretrained("my-awesome-model")
>>> # Push model weights to the Hub
>>> model.push_to_hub("my-awesome-model")
>>> # Download and initialize weights from the Hub
>>> model = MyModel.from_pretrained("username/my-awesome-model")

Keras

class huggingface_hub.KerasModelHubMixin

< source >

( )

Implementation of ModelHubMixin to provide model Hub upload/download capabilities to Keras models.

>>> import tensorflow as tf
>>> from huggingface_hub import KerasModelHubMixin


>>> class MyModel(tf.keras.Model, KerasModelHubMixin):
...     def __init__(self, **kwargs):
...         super().__init__()
...         self.config = kwargs.pop("config", None)
...         self.dummy_inputs = ...
...         self.layer = ...

...     def call(self, *args):
...         return ...


>>> # Initialize and compile the model as you normally would
>>> model = MyModel()
>>> model.compile(...)
>>> # Build the graph by training it or passing dummy inputs
>>> _ = model(model.dummy_inputs)
>>> # Save model weights to local directory
>>> model.save_pretrained("my-awesome-model")
>>> # Push model weights to the Hub
>>> model.push_to_hub("my-awesome-model")
>>> # Download and initialize weights from the Hub
>>> model = MyModel.from_pretrained("username/super-cool-model")

huggingface_hub.from_pretrained_keras

< source >

( *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.
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 token=True is required when you want to use a private model.

huggingface_hub.push_to_hub_keras

< source >

( model repo_path_or_name: typing.Optional[str] = None repo_url: typing.Optional[str] = None log_dir: typing.Optional[str] = None commit_message: str = 'Add Keras model' organization: typing.Optional[str] = None private: bool = False api_endpoint: typing.Optional[str] = None git_user: typing.Optional[str] = None git_email: typing.Optional[str] = None config: typing.Optional[dict] = None include_optimizer: bool = False tags: typing.Union[list, str, NoneType] = None plot_model: bool = True token: typing.Optional[str] = None repo_id: typing.Optional[str] = None branch: typing.Optional[str] = None create_pr: typing.Optional[bool] = None allow_patterns: typing.Union[typing.List[str], str, NoneType] = None ignore_patterns: typing.Union[typing.List[str], str, NoneType] = None **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_id (str) — Repository name to which push
commit_message (str, optional, defaults to “Add Keras model”) — Message to commit while pushing.
private (bool, optional, defaults to False) — Whether the repository created should be private.
api_endpoint (str, optional) — The API endpoint to use when pushing the model to the hub.
token (str, optional) — The token to use as HTTP bearer authorization for remote files. If not set, will use the token set when logging in with huggingface-cli login (stored in ~/.huggingface).
branch (str, optional) — The git branch on which to push the model. This defaults to the default branch as specified in your repository, which defaults to "main".
create_pr (boolean, optional) — Whether or not to create a Pull Request from branch with that commit. Defaults to False.
config (dict, optional) — Configuration object to be saved alongside the model weights.
allow_patterns (List[str] or str, optional) — If provided, only files matching at least one pattern are pushed.
ignore_patterns (List[str] or str, optional) — If provided, files matching any of the patterns are not pushed.
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.
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.

Use allow_patterns and ignore_patterns to precisely filter which files should be pushed to the hub. See upload_folder() reference for more details.

huggingface_hub.save_pretrained_keras

< source >

( model save_directory: typing.Union[str, pathlib.Path] config: typing.Union[typing.Dict[str, typing.Any], NoneType] = None include_optimizer: bool = False plot_model: 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 or Path) — 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.

Fastai

huggingface_hub.from_pretrained_fastai

< source >

( 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

< source >

( learner repo_id: str commit_message: str = 'Add model' private: bool = False token: typing.Optional[str] = None config: typing.Optional[dict] = None branch: typing.Optional[str] = None create_pr: typing.Optional[bool] = None allow_patterns: typing.Union[typing.List[str], str, NoneType] = None ignore_patterns: typing.Union[typing.List[str], str, NoneType] = None api_endpoint: typing.Optional[str] = None git_user: typing.Optional[str] = None git_email: typing.Optional[str] = None )

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, defaults to False) — 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.
branch (str, optional) — The git branch on which to push the model. This defaults to the default branch as specified in your repository, which defaults to “main”.
create_pr (boolean, optional) — Whether or not to create a Pull Request from branch with that commit. Defaults to False.
api_endpoint (str, optional) — The API endpoint to use when pushing the model to the hub.
allow_patterns (List[str] or str, optional) — If provided, only files matching at least one pattern are pushed.
ignore_patterns (List[str] or str, optional) — If provided, files matching any of the patterns are not pushed.

Upload learner checkpoint files to the Hub.

Use allow_patterns and ignore_patterns to precisely filter which files should be pushed to the hub. See [upload_folder] reference for more details.

Raises the following error:

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

Hub Python Library

Mixins & serialization methods

Mixins

Generic

class huggingface_hub.ModelHubMixin

_save_pretrained

_from_pretrained

from_pretrained

push_to_hub

save_pretrained

PyTorch

class huggingface_hub.PyTorchModelHubMixin

Keras

class huggingface_hub.KerasModelHubMixin

huggingface_hub.from_pretrained_keras

huggingface_hub.push_to_hub_keras

huggingface_hub.save_pretrained_keras

Fastai

huggingface_hub.from_pretrained_fastai

huggingface_hub.push_to_hub_fastai