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.

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 .

Then came version 0.28.0 and the Inference Providers ecosystem. Instead of a single inference backend, we partnered with multiple serverless providers (Together AI, SambaNova, Replicate, Cerebras, Groq, and more) to serve one API with transparent routing. We adopted a pay-per-request inference architecture that matched how people actually wanted to work.

Why httpx? The benefits are substantial: native HTTP/2 support for better connection efficiency and true thread safety that enables safe connection reuse across multiple threads. Most importantly, httpx provides a unified API for both synchronous and asynchronous operations, eliminating the subtle behavioral differences that existed between our sync and async inference clients.

With autocompletion support and an installer that works across platforms, the CLI now feels as polished as any modern developer tool.

To our 280+ contributors who built this library through code, documentation, translations, and community support, thank you!

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 .

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.