Bev v0.1.1

Bev v0.1.1

Bev brings Jevfire-style one-token decision scoring to Prism ML's unchanged Ternary-Bonsai-2-27B PQ2_0 model. This release packages the inference service and reproducible configuration; it does not introduce trained or fine-tuned weights.

• Typed decisions: /v1/decisions for flat boolean/enum schemas; /v1/systemone for Choice, Noul and Score. Original option identities and rubric descriptions are preserved. Score is the probability-weighted rubric position.
• Complete candidate scores: a small Prism runtime patch returns selected raw token log probabilities while retaining full-vocabulary normalization. Missing candidates and oversized contexts fail explicitly.
• Auditable service: startup-captured source hashes, pinned model/runtime files, and a benchmark wrapper that checks source stability across evaluation.
• Validation: 53 Bev/package tests and 66 upstream Persian benchmark tests passed. The full Persian run completed 624/624 valid answers, with 229/240 Choice, 152/160 Noul, and 70/80 Score within ±0.5.
• Measured footprint: 7,206,168,928-byte GGUF; approximately 8,504 MiB observed serving-process GPU memory on an RTX 5080 Laptop GPU. Persian benchmark median latency was 2.136 seconds per request, with six questions in main/repeat requests.

Candidate probabilities are relative preferences, not calibrated correctness guarantees. Fields are evaluated independently. Quality trails the published Jev reference on this Persian suite, and the earlier 120-request general diagnostic shows mixed results. No controlled full-precision comparison or ternary speedup claim is made.

See benchmark results and limitations, aggregate Persian results, and provenance. Benchmark texts, labels and upstream scorer code are not bundled.