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# Hugging Face Introduces Community Evals for Transparent Model Benchmarking

**[InfoQ](https://daily.dev/sources/infoq)** · 3 min read · 0 upvotes · 0 comments

## Summary

Hugging Face has launched Community Evals, a beta feature that lets benchmark datasets on the Hub host their own leaderboards and automatically aggregate evaluation results from model repositories. Benchmarks define evaluation specs via an `eval.yaml` file using the Inspect AI format, ensuring reproducibility. Model authors and community members can submit scores through pull requests, with all changes versioned via Git for full traceability. Initial supported benchmarks include MMLU-Pro, GPQA, and HLE. The system aims to address inconsistencies in reported benchmark scores across papers and platforms by making evaluation reporting decentralized, transparent, and standardized.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.infoq.com/news/2026/02/hugging-face-evals/>

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