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

model-hub, machine-learning, models, natural-language-processing, deep-learning, pytorch, pretrained-models, hacktoberfest
pip install huggingface-hub==0.12.0



Code style: black Code coverage GitHub release Documentation Documentation

Welcome to the huggingface_hub library

The huggingface_hub is a client library to interact with the Hugging Face Hub. The Hugging Face Hub is a platform with over 90K models, 14K datasets, and 12K demos in which people can easily collaborate in their ML workflows. The Hub works as a central place where anyone can share, explore, discover, and experiment with open-source Machine Learning.

With huggingface_hub, you can easily download and upload models, datasets, and Spaces. You can extract useful information from the Hub, and do much more. Some example use cases:

  • Downloading and caching files from a Hub repository.
  • Creating repositories and uploading an updated model every few epochs.
  • Extract metadata from all models that match certain criteria (e.g. models for text-classification).
  • List all files from a specific repository.

Read all about it in the library documentation.

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.
  • Hosted 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 โค๏ธ showing how to do this integration.

Feedback (feature requests, bugs, etc.) is super welcome ๐Ÿ’™๐Ÿ’š๐Ÿ’›๐Ÿ’œโ™ฅ๏ธ๐Ÿงก