Featuring Every Eval Ever Results on Hugging Face Model Pages
Every Eval Ever's ~229k benchmark results across 22k+ models now cross-post to Hugging Face model pages with attribution — one less reason for the same model to show two different MMLU scores.
**The gist** Hugging Face and the EvalEval Coalition made **Every Eval Ever** interoperable with HF Community Evals: a converter maps EEE's JSON into HF's YAML and opens PRs against model pages. The datastore holds about **229,000 results** across **22,000+ models** and 2,200 benchmarks pulled from 31 reporting formats; four benchmarks are wired up so far: **MMLU-Pro, GPQA, HLE, and GSM8K**.
**Why it matters** Model selection today means trusting whichever score a vendor or leaderboard reports — **LLaMA 65B** has been listed at both **63.7 and 48.8** on MMLU. Attributed, aggregated results on the model page give you a sanity check before swapping the model behind your agents, from a corpus that would cost **hundreds of thousands of dollars** to reproduce.
**The gist** Hugging Face and the EvalEval Coalition made **Every Eval Ever** interoperable with HF Community Evals: a converter maps EEE's JSON into HF's YAML and opens PRs against model pages. The datastore holds about **229,000 results** across **22,000+ models** and 2,200 benchmarks pulled from 31 reporting formats; four benchmarks are wired up so far: **MMLU-Pro, GPQA, HLE, and GSM8K**. **Why it matters** Model selection today means trusting whichever score a vendor or leaderboard reports — **LLaMA 65B** has been listed at both **63.7 and 48.8** on MMLU. Attributed, aggregated results on the model page give you a sanity check before swapping the model behind your agents, from a corpus that would cost **hundreds of thousands of dollars** to reproduce. **Watch out** Coverage is thin: only **four benchmarks** convert today with **no stated timeline** for more, and aggregation surfaces conflicting scores without resolving which number is right.