Every Eval Ever and Hugging Face Community Evals have become intercompatible, offering cross-posting and interpretation of AI evaluation results alongside links to open models, leaderboards, and a unified metadata store. Launched in February 2026, Every Eval Ever serves as a project of the EvalEval Coalition to standardize how first and third-party evaluators report AI evaluations. Hugging Face concurrently launched Community Evals to decentralize benchmark score reporting on its Hub. Evaluation results have historically been scattered across papers, leaderboards, blog posts, and harness logs in disparate formats, frequently leading to discrepancies like differing scores for LLaMA 65B on MMLU due to unreported settings. Every Eval Ever addresses this reporting gap using a single JSON schema designed with feedback from researchers. Its datastore has grown to around 229,000 evaluation results across more than 22,000 models and 2,200 benchmarks from 31 reporting formats. A newly developed converter translates EEE records into the YAML files expected by Hugging Face Community Evals, automating cross-compatibility. Contributors can send EEE results to Community Evals, allowing scores to appear on Hugging Face model pages and benchmark leaderboards while carrying a source badge linking back to the full EEE record. The converter audits existing entries on model repositories, checking for duplicates, conflicts, or missing models before generating local previews and review files. It requires explicit user confirmation via a prompt before opening pull requests. Full documentation and tools are available through the GitHub repository and the EvalEval website.
Featuring Every Eval Ever Results on Hugging Face Model Pages
Every Eval Ever and Hugging Face Community Evals are now intercompatible, enabling cross-posting and interpretation of AI evaluation results. The integration links open models, leaderboards, and a unified standardized metadata store. Both projects launched in February 2026 to improve how benchmark scores and evaluation results are reported.
Why it matters
This integration bridges the gap between scattered AI evaluation metrics and centralized model hubs, improving transparency and making benchmark scores easier to trust, compare, and reproduce across the industry.