Featuring Every Eval Ever Results on Hugging Face Model Pages
Featuring Every Eval Ever Results on Hugging Face Model Pages Featuring Every Eval Ever Results on Hugging Face Model Pages Published June 30, 2026 Update on GitHub Upvote 54 Sree Harsha Nelaturu deepmage121 evaleval Avijit Ghosh evijit Nathan Habib SaylorTwift Jan Batzner janbatzner evaleval Leshem Choshen borgr evaleval Irene Solaiman irenesolaiman Julien Chaumond julien-c Every Eval Ever (EEE) and Hugging Face Community Evals are now intercompatible. We enable cross-posting and interpreting evaluation results, while linking to open models, leaderboards, and a unified standardized metadata store.
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- Featuring Every Eval Ever Results on Hugging Face Model Pages Published June 30, 2026 Update on GitHub Upvote 54 Sree Harsha Nelaturu deepmage121 evaleval Avijit Ghosh evijit Nathan Habib SaylorTwift Jan Batzner janbatzner evaleval Leshem Choshen borgr evaleval Irene Solaiman irenesolaiman Julien Chaumond julien-c Every Eval Ever (EEE) and Hugging Face Community Evals are now intercompatible.
- We enable cross-posting and interpreting evaluation results, while linking to open models, leaderboards, and a unified standardized metadata store.
- EEE launched in February 2026 as a project of the EvalEval Coalition , the first cross-institutional effort to improve how AI evaluation results get reported by both first and third party evaluators.
- Hugging Face launched Community Evals in February 2026 to decentralize how benchmark scores get reported on the Hub.
- Combined, they patch gaps in how users, researchers, and policymakers trust, understand, and choose evaluations and models.
- Evaluation results are how we measure model capabilities, compare models against each other, and reason about safety and governance, and yet they are scattered and hard to compare.