The Hugging Face Hub makes hosting and sharing models with the community easy. It supports
dozens of libraries in the Open Source ecosystem. We are always
working on expanding this support to push collaborative Machine Learning forward. The
huggingface_hub library plays a
key role in this process, allowing any Python script to easily push and load files.
There are four main ways to integrate a library with the Hub:
- Push to Hub: implement a method to upload a model to the Hub. This includes the model weights, as well as
the model card and any other relevant information
or data necessary to run the model (for example, training logs). This method is often called
- Download from Hub: implement a method to load a model from the Hub. The method should download the model
configuration/weights and load the model. This method is often called
- Inference API: use our servers to run inference on models supported by your library for free.
- Widgets: display a widget on the landing page of your models on the Hub. It allows users to quickly try a model from the browser.
In this guide, we will focus on the first two topics. We will present the two main approaches you can use to integrate a library, with their advantages and drawbacks. Everything is summarized at the end of the guide to help you choose between the two. Please keep in mind that these are only guidelines that you are free to adapt to you requirements.
The first approach to integrate a library to the Hub is to actually implement the
methods by yourself. This gives you full flexibility on which files you need to upload/download and how to handle inputs
specific to your framework. You can refer to the two upload files and download files guides
to learn more about how to do that. This is, for example how the FastAI integration is implemented (see push_to_hub_fastai()
Implementation can differ between libraries, but the workflow is often similar.
This is how a
from_pretrained method usually look like:
def from_pretrained(model_id: str) -> MyModelClass: # Download model from Hub cached_model = hf_hub_download( repo_id=repo_id, filename="model.pkl", library_name="fastai", library_version=get_fastai_version(), ) # Load model return load_model(cached_model)
push_to_hub method often requires a bit more complexity to handle repo creation, generate the model card and save weights.
A common approach is to save all of these files in a temporary folder, upload it and then delete it.
def push_to_hub(model: MyModelClass, repo_name: str) -> None: api = HfApi() # Create repo if not existing yet and get the associated repo_id repo_id = api.create_repo(repo_name, exist_ok=True) # Save all files in a temporary directory and push them in a single commit with TemporaryDirectory() as tmpdir: tmpdir = Path(tmpdir) # Save weights save_model(model, tmpdir / "model.safetensors") # Generate model card card = generate_model_card(model) (tmpdir / "README.md").write_text(card) # Save logs # Save figures # Save evaluation metrics # ... # Push to hub return api.upload_folder(repo_id=repo_id, folder_path=tmpdir)
This is of course only an example. If you are interested in more complex manipulations (delete remote files, upload weights on the fly, persist weights locally, etc.) please refer to the upload files guide.
While being flexible, this approach has some drawbacks, especially in terms of maintenance. Hugging Face users are often
used to additional features when working with
huggingface_hub. For example, when loading files from the Hub, it is
common to offer parameters like:
token: to download from a private repo
revision: to download from a specific branch
cache_dir: to cache files in a specific directory
local_files_only: to reuse the cache or not
proxies: configure HTTP session
When pushing models, similar parameters are supported:
commit_message: custom commit message
private: create a private repo if missing
create_pr: create a PR instead of pushing to
branch: push to a branch instead of the
ignore_patterns: filter which files to upload
All of these parameters can be added to the implementations we saw above and passed to the
However, if a parameter changes or a new feature is added, you will need to update your package. Supporting those
parameters also means more documentation to maintain on your side. To see how to mitigate these limitations, let’s jump
to our next section class inheritance.
As we saw above, there are two main methods to include in your library to integrate it with the Hub: upload files
push_to_hub) and download files (
from_pretrained). You can implement those methods by yourself but it comes with
caveats. To tackle this,
huggingface_hub provides a tool that uses class inheritance. Let’s see how it works!
In a lot of cases, a library already implements its model using a Python class. The class contains the properties of
the model and methods to load, run, train, and evaluate it. Our approach is to extend this class to include upload and
download features using mixins. A Mixin is a class that is meant to extend an
existing class with a set of specific features using multiple inheritance.
huggingface_hub provides its own mixin,
the ModelHubMixin. The key here is to understand its behavior and how to customize it.
The ModelHubMixin class implements 3 public methods (
are the methods that your users will call to load/save models with your library. ModelHubMixin also defines 2
private methods (
_from_pretrained). Those are the ones you must implement. So to integrate
your library, you should:
- Make your Model class inherit from ModelHubMixin.
- Implement the private methods:
- _save_pretrained(): method taking as input a path to a directory and saving the model to it. You must write all the logic to dump your model in this method: model card, model weights, configuration files, training logs, and figures. Any relevant information for this model must be handled by this method. Model Cards are particularly important to describe your model. Check out our implementation guide for more details.
- _from_pretrained(): class method taking as input a
model_idand returning an instantiated model. The method must download the relevant files and load them.
- You are done!
The advantage of using ModelHubMixin is that once you take care of the serialization/loading of the files, you are ready to go. You don’t need to worry about stuff like repo creation, commits, PRs, or revisions. All of this is handled by the mixin and is available to your users. The Mixin also ensures that public methods are well documented and type annotated.
A good example of what we saw above is PyTorchModelHubMixin, our integration for the PyTorch framework. This is a ready-to-use integration.
Here is how any user can load/save a PyTorch model from/to the Hub:
import torch import torch.nn as nn from huggingface_hub import PyTorchModelHubMixin # 1. Define your Pytorch model exactly the same way you are used to class MyModel(nn.Module, PyTorchModelHubMixin): # multiple inheritance 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() # 2. (optional) Save model to local directory model.save_pretrained("path/to/my-awesome-model") # 3. Push model weights to the Hub model.push_to_hub("my-awesome-model") # 4. Initialize model from the Hub model = MyModel.from_pretrained("username/my-awesome-model")
The implementation is actually very straightforward, and the full implementation can be found here.
- First, inherit your class from
from huggingface_hub import ModelHubMixin class PyTorchModelHubMixin(ModelHubMixin): (...)
- Implement the
from huggingface_hub import ModelCard, ModelCardData class PyTorchModelHubMixin(ModelHubMixin): (...) def _save_pretrained(self, save_directory: Path): """Generate Model Card and save weights from a Pytorch model to a local directory.""" model_card = ModelCard.from_template( card_data=ModelCardData( license='mit', library_name="pytorch", ... ), model_summary=..., model_type=..., ... ) (save_directory / "README.md").write_text(str(model)) torch.save(obj=self.module.state_dict(), f=save_directory / "pytorch_model.bin")
- Implement the
class PyTorchModelHubMixin(ModelHubMixin): (...) # Must be a classmethod! def _from_pretrained( cls, *, model_id: str, revision: str, cache_dir: str, force_download: bool, proxies: Optional[Dict], resume_download: bool, local_files_only: bool, token: Union[str, bool, None], map_location: str = "cpu", # additional argument strict: bool = False, # additional argument **model_kwargs, ): """Load Pytorch pretrained weights and return the loaded model.""" if os.path.isdir(model_id): # Can either be a local directory print("Loading weights from local directory") model_file = os.path.join(model_id, "pytorch_model.bin") else: # Or a model on the Hub model_file = hf_hub_download( # Download from the hub, passing same input args repo_id=model_id, filename="pytorch_model.bin", revision=revision, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, token=token, local_files_only=local_files_only, ) # Load model and return - custom logic depending on your framework model = cls(**model_kwargs) state_dict = torch.load(model_file, map_location=torch.device(map_location)) model.load_state_dict(state_dict, strict=strict) model.eval() return model
And that’s it! Your library now enables users to upload and download files to and from the Hub.
Let’s quickly sum up the two approaches we saw with their advantages and drawbacks. The table below is only indicative. Your framework might have some specificities that you need to address. This guide is only here to give guidelines and ideas on how to handle integration. In any case, feel free to contact us if you have any questions!
|Integration||Using helpers||Using ModelHubMixin|
You fully control the implementation.
Your framework must have a model class.
|Maintenance||More maintenance to add support for configuration, and new features. Might also require fixing issues reported by users.||Less maintenance as most of the interactions with the Hub are implemented in |
|Documentation / Type annotation||To be written manually.||Partially handled by |