huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning

TL;DR: After five years of development, huggingface_hub has reached v1.0 - a milestone that marks the library's maturity as the Python package powering 200,000 dependent libraries and providing core functionality for accessing over 2 million public models, 0.5 million public datasets, and 1 million public Spaces. This release introduces breaking changes designed to support the next decade of open machine learning, driven by a global community of almost 300 contributors and millions of users.

🚀 We highly recommend upgrading to v1.0 to benefit from major performance improvements and new capabilities.

pip install --upgrade huggingface_hub

Major changes in this release include the migration to httpx as the backend library, a completely redesigned hf CLI (which replaces the deprecated huggingface-cli) featuring a Typer-based interface with a significantly expanded feature set, and full adoption of hf_xet for file transfers, replacing the legacy hf_transfer. You can find the full release notes here.

We’ve worked hard to ensure that huggingface_hub v1.0.0 remains backward compatible. In practice, most ML libraries should work seamlessly with both v0.x and v1.x versions. The main exception is transformers, which explicitly requires huggingface_hub v0.x in its v4 releases and v1.x in its upcoming v5 release. For a detailed compatibility overview across libraries, refer to the table in this issue.

The Story Behind the Library

Every major library has a story. For huggingface_hub, it began with a simple idea: what if sharing machine learning models could be as easy as sharing code on GitHub?

The Hugging Face Hub emerged as the answer to this challenge. Initially, it was primarily used to share checkpoints compatible with the transformers library. All the Python code for interacting with the Hub lived within this library, making it inaccessible for other libraries to reuse.

Let's trace that journey.