đ 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.
Weâve 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.
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.
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