README.md

metadata

title: Quorum
emoji: ⚖️
colorFrom: red
colorTo: gray
sdk: static
pinned: false
license: mit

Quorum — ask the jury

A jury of three tiny open models reads a message, each casts a vote, and Quorum returns the verdict with a confidence you can act on. Every example on the page is a real test sentence from one of seven public intent benchmarks (Banking77, CLINC150, HWU64, MASSIVE, MTOP, SNIPS, Bitext), so you can see when the jury gets it right — and when it doesn't. You can also type your own sentence and labels.

• The page is static (index.html + samples.json) and calls a self-hosted Quorum API. Benchmark examples are answered instantly from the API's cache; your own sentences are computed live (the first one after a quiet spell takes a minute or two while the models wake up). Sentences you type are kept on the demo server so repeats answer instantly.
• Run your own API: see serve/ and set DEFAULT_API in your copy of index.html (when the page is served from localhost, ?api=https://your-api.example also works).
• samples.json is generated from the per-item benchmark predictions: python -m serve.export_demo_samples.
• Code + study: https://github.com/DataAgent-Lab/Quorum · Collection: https://huggingface.co/collections/DataAgent/quorum-6abb3255e0fab20d66add39b

Honest note. This is the zero-shot tier — cheap and calibrated, but not open zero-shot state-of-the-art: the closed API Jev (an independent reproduction, ~0.801 on Banking77) still leads zero-shot. The 24-shot pipeline that edges it (0.932 vs 0.924 on Banking77) uses a gated LoRA adapter, DataAgent/Quorum-Reader-Qwen3-4B-Adapter.