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Client library to download and publish models, datasets and other repos on the hub

Project description

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The official Python client for the Huggingface Hub.

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Welcome to the huggingface_hub library

The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python.

Key features


Install the huggingface_hub package with pip:

pip install huggingface_hub

If you prefer, you can also install it with conda.

In order to keep the package minimal by default, huggingface_hub comes with optional dependencies useful for some use cases. For example, if you want have a complete experience for Inference, run:

pip install huggingface_hub[inference]

To learn more installation and optional dependencies, check out the installation guide.

Quick start

Download files

Download a single file

from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="tiiuae/falcon-7b-instruct", filename="config.json")

Or an entire repository

from huggingface_hub import snapshot_download


Files will be downloaded in a local cache folder. More details in this guide.


The Hugging Face Hub uses tokens to authenticate applications (see docs). To login your machine, run the following CLI:

huggingface-cli login
# or using an environment variable
huggingface-cli login --token $HUGGINGFACE_TOKEN

Create a repository

from huggingface_hub import create_repo


Upload files

Upload a single file

from huggingface_hub import upload_file


Or an entire folder

from huggingface_hub import upload_folder


For details in the upload guide.

Integrating to the Hub.

We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations here.

The advantages are:

  • Free model or dataset hosting for libraries and their users.
  • Built-in file versioning, even with very large files, thanks to a git-based approach.
  • Serverless inference API for all models publicly available.
  • In-browser widgets to play with the uploaded models.
  • Anyone can upload a new model for your library, they just need to add the corresponding tag for the model to be discoverable.
  • Fast downloads! We use Cloudfront (a CDN) to geo-replicate downloads so they're blazing fast from anywhere on the globe.
  • Usage stats and more features to come.

If you would like to integrate your library, feel free to open an issue to begin the discussion. We wrote a step-by-step guide with ❤️ showing how to do this integration.

Contributions (feature requests, bugs, etc.) are super welcome 💙💚💛💜🧡❤️

Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community. We wrote a contribution guide to summarize how to get started to contribute to this repository.

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