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.

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.

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?

In the early days of the Hugging Face Hub, researchers and practitioners faced a common frustration. Training a state-of-the-art model required significant compute resources and expertise. Once trained, these models often lived in isolation, stored on local machines and shared via (broken) Google Drive links. The AI community was duplicating work, wasting resources, and missing opportunities for collaboration.

The Hugging Face Hub emerged as the answer to this challenge. Initially, it was primarily used to share checkpoints compatible with the transformers library. In late 2020, we shipped huggingface_hub v0.0.1 with a simple mission: extract the internal logic from transformers and create a dedicated library that would unify how to access and share machine learning models and datasets.

In June 2022, version 0.8.1 marked a pivotal moment: we introduced the HTTP Commit API. Instead of requiring Git and Git LFS installations, users could now upload files directly through HTTP requests. This wasn't just a technical improvement. It was a philosophical shift. We were no longer building a Git wrapper; we were building purpose-built infrastructure for machine learning artifacts.

Version 0.30.0 introduced Xet, a groundbreaking new protocol for storing large objects. Unlike Git LFS, which deduplicates at the file level, Xet operates at the chunk level (64KB chunks). When you update a large file, only the changed chunks are uploaded or downloaded. One year later, all 77PB+ over 6,000,000 repositories have been migrated to the Xet backend transparently.

Measuring the growth and impact:

• 113.5 million monthly downloads, 1.6 billion total (October 2025).
• Powers access to 2M+ public models, 500k+ public datasets, 1M+ public Spaces.
• Used by 60k+ users daily, 550k+ monthly.
• Trusted by 200k+ companies from startups to Fortune 500.

Version 1.0 isn't just about reaching a milestone. It's about building the foundation for the next decade of open machine learning. The breaking changes we've made are strategic decisions that position huggingface_hub to scale with the explosive growth of AI while maintaining the reliability that millions of developers depend on.