Bev v0.1.2 — measured RAM and portable runtime

Bev v0.1.2

Memory first: the 7.21 GB GGUF reached 7.28 GiB peak resident system RAM in a short CPU-only inference with one 4,096-token slot on Stallion. The earlier 8.30 GiB number is an observed GPU-memory snapshot for the separate Linux CUDA benchmark setup with two 16,384-token slots; it is not host RAM or a measured peak. Allow additional headroom.

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: 46 Bev tests passed and 7 optional-dependency tests were skipped on macOS; 14/14 real Metal API checks passed. The v0.1.1 Linux CUDA run previously passed 53 Bev/package tests and 66 upstream Persian benchmark tests. The full Persian run completed 624/624 valid answers, with 229/240 Choice, 152/160 Noul, and 70/80 Score within ±0.5.
• Portable build: the same pinned Prism source and scoring patch can now be built through scripts/install-portable.py for macOS Metal or Linux/Windows CPU. 14/14 API smoke checks passed on an Apple M2 Mac; Windows remains an untested source-build path. The Linux CUDA path remains available.
• Measured footprint: 7,206,168,928-byte GGUF; 7,630,416 KiB CPU process high-water RSS on Stallion and 8,504 MiB observed GPU memory in the original CUDA setup. 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.

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.