Changelog

You can now publish to Hugging Face repositories from GitHub, GitLab, or custom CI systems without adding secrets, using workflow identity federation.

This mirrors the secret‑less, Trusted Publishing experience available for npm and PyPI. CI jobs can also read from gated repositories that the user has access to. See the documentation

A new Base only toggle on the Models page hides every finetune, adapter, merge, and quantization, leaving just the original base models.

Need the opposite? The Other → Model Tree filter does the reverse, listing only one relation type across the Hub, like every adapter or every quantized model.

You can now copy the contents of any repository directly from the Hub to a Bucket using the new “Copy to Bucket” button on repository pages. Powered by Xet server-side transfers, large files are copied instantly, making it possible to transfer terabytes in just a few seconds. Read more about copying files between repos and buckets.

For example, you can now copy massive datasets or model checkpoints from the Hub into your Bucket, then mount the Bucket directly in your training jobs or Spaces to start working immediately. Read more about use cases.

You can now filter dataset benchmark leaderboards by the number of model parameters. On any dataset leaderboard, pick a size range and the rankings refresh to that bucket. The top 3 models in each size category are marked with a 🏅.

Every Gradio Space now auto-serves an /agents.md endpoint, a machine-readable API description that AI agents can read and call directly. Point your coding agents (like Claude Code, Codex, or Pi) at it and they figure out how to use the Space without any setup. Read the documentation to learn more.