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Featuring Every Eval Ever Results on Hugging Face Model Pages

02:00 · June 30, 2026 · Hugging Face Blog

Featuring Every Eval Ever Results on Hugging Face Model Pages

Summary

Hugging Face has combined its Community Evals system with the Every Eval Ever project to create a shared pathway for publishing evaluation results on the Hub. Every Eval Ever, launched in February 2026 by the EvalEval Coalition, supplies a single JSON schema that captures evaluation metadata from any source, including harness logs, leaderboard scrapes, and published papers. The schema records generation settings, harness versions, and reproducibility details that are often omitted from conventional reporting.

Community Evals, introduced at the same time, lets benchmark scores appear directly on model cards and on dataset-specific leaderboards. Model results are stored as small YAML files inside each model repository under .eval_results, while benchmarks register themselves through an eval.yaml file in their dataset repository. Scores can be contributed by model authors or by third parties via pull requests, and each entry carries a badge indicating whether it is author-submitted, community-submitted, or independently verified.

A dedicated converter now maps Every Eval Ever records to the required YAML format. It identifies matching entries for four supported benchmarks—MMLU-Pro, GPQA, HLE, and GSM8K—audits existing files to avoid duplicates or conflicts, and produces local previews for review before any pull request is opened. Once submitted through an official organization account, the result appears on the model page with a source link that points back to the full Every Eval Ever JSON record.

The combined workflow addresses the long-standing problem of inconsistent benchmark numbers. The same model and benchmark can yield markedly different scores depending on unreported evaluation settings, as illustrated by the spread between 63.7 and 48.8 on MMLU for LLaMA 65B. By storing both the compact Hub representation and the richer structured record, the integration makes results simultaneously visible where practitioners look for models and traceable for anyone who needs the underlying configuration details.

Why it matters

This article provides ML Engineers with a concrete, actionable MLOps tool to standardize model evaluation and benchmarking. By adopting the EEE schema, Dutch AI teams can ensure reproducibility and transparency in their model deployments, which is increasingly important for compliance with EU AI regulations and building trust in enterprise AI solutions.

More in this beat
community-evalsevaluation-benchmarksEvery Eval Everhugging-facemmlu-prorelease-highlightsreproducibility-assets
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