Bev

7.21 GB model file · 7.28 GiB measured CPU RAM · 8.30 GiB measured VRAM.

A ternary decision engine built around Jevfire-style one-token scoring.

Code & documentation · Release v0.1.2 · Benchmarks

The GGUF in this repository is a byte-identical redistribution of Prism ML's Ternary-Bonsai-2-27B. Bev did not train or quantize these weights. Prism supplies the ternary model, derived from Qwen3.8-27B. Bev adds a selected-token scoring extension, a local typed-decision API, portable setup, and measured evaluation. This is a model-and-software bundle, not a new fine-tune.

Memory and platforms

The Mac figure is sampled process RSS, not total unified-memory pressure or a guaranteed peak. The CPU number is Linux VmHWM of 7,630,416 KiB, including memory-mapped model pages; loaded idle was about 7.12 GiB. The GPU number is 8,504 MiB from a separate run and is not a peak or a host-RAM figure. The file itself is 6.71 GiB on disk. CPU-only recommendation: start with 16 GB system RAM. An 8 GB machine is unverified and likely too tight. Allow headroom for the OS, API, longer contexts, and parallelism. Apple Silicon uses unified memory; RAM and Metal allocations cannot be added as separate device pools. See installation and memory details.

What Bev does

Provide context and finite choices. Bev evaluates one next-token distribution per field, scores every candidate, and assembles structured JSON in Python. It supports:

The runtime supports 2–255 candidates per field. The validated serving configuration has two slots and 16,384 tokens per field. The API rejects oversized inputs and incomplete score sets explicitly.

Files and provenance

The model-manifest.json records model/runtime pins and checksums. bev-v0.1.2-source.tar.gz contains the complete portable source, examples, tests, runtime patch, and documentation. The Python wheel packages the API only; the source installer is needed to set up the native backend. SHA256SUMS covers downloadable release artifacts.

Run it

The fully benchmarked setup is Linux x86_64 with an NVIDIA GPU and the pinned Prism CUDA 12.8 runtime. Portable patched-source builds are supplied for macOS Metal and Windows/Linux CPU. Use each platform’s validation status above; the same Persian accuracy numbers are not yet independently reproduced on macOS or Windows.

Use the installation guide for prerequisites, then:

git clone --branch v0.1.2 https://github.com/Reza2kn/Bev.git
cd Bev
bash scripts/install.sh
bash scripts/start-services.sh

curl --fail-with-body http://127.0.0.1:18781/v1/decisions \
  -H 'Content-Type: application/json' \
  --data-binary @examples/support-request.json

The support example returns {"route":"billing"} in parsed_json, alongside complete candidate scores. Interactive API documentation is served at http://127.0.0.1:18781/docs. The API binds to loopback by default.

For macOS or Windows/Linux CPU, follow the portable installation instructions. The installer verifies and downloads the original pinned Prism file. To use the identical copy from this repository instead, download it into the same model directory before installation:

export BEV_ROOT="${BEV_ROOT:-${XDG_DATA_HOME:-$HOME/.local/share}/bev}"
hf download Reza2kn/Bev Ternary-Bonsai-2-27B-PQ2_0.gguf --local-dir "$BEV_ROOT/models"

This requires the Hugging Face CLI (pip install huggingface_hub). A generic GGUF viewer or stock upstream llama.cpp is not the validated runtime for PQ2_0. Use the pinned Prism fork and Bev adapter. This repository does not supply a Transformers classification head, a hosted inference endpoint, or a browser demo.

Persian evaluation

On September 23, 2026, Bev v0.1.1 (same model and scoring code as v0.1.2) ran the complete Jev Persian Benchmark at commit ac218d96630da9d9cc08fd897868c4d3c7048b0d, using the original dataset, question order, batches and scorer. All 624/624 answers were valid across 106/106 completed requests.

Jev numbers are the benchmark author's published reference, not an independent Jev run here. The main evaluation has 480 questions; English and repeat diagnostics are separate. Bev had zero decision changes across 48 three-observation repeat groups, while some probabilities varied slightly. No training, prompt selection or calibration fitting used these cases.

The measured median was 2.136 seconds per request and total request time 224.03 seconds. Main/repeat batches each have six questions. The hosted Jev reference and this laptop GPU have different hardware and serving conditions. No matched full-precision or ternary speedup comparison was performed.

Aggregate results and provenance are included under evaluations/. Raw benchmark questions, gold labels, original scorer code, request journals and private host details are excluded. Reproduction instructions use the separately obtained upstream benchmark.

Limits and intended use

Bev is intended for finite-label routing, classification and rubric evaluation where the application can define the allowed outputs. Fields are independent. Relative candidate probabilities are not calibrated confidence in correctness; confident mistakes occurred in evaluation.

The Persian benchmark is synthetic and correlated, without independent human annotation. An earlier small general diagnostic scored 7/12 MMLU and 2/10 SimpleBench, alongside stronger results on other small subsets. Its loaded-source attestation was incomplete; the full report retains this limitation. Neither run establishes broad reliability, Jev parity, or a Decision Index rank.

One-token scoring can miss problems requiring multi-step reasoning, and the model inherits limitations and biases from its upstream models. A constrained output format does not guarantee a correct decision. No new calibration or independent production-domain validation is supplied by this release.

Attribution

• Jevfire / kikoncuo: finite-choice scoring method and classification prompt, MIT.
• Prism ML: Ternary-Bonsai-2 model and the Prism llama.cpp fork.
• Qwen / Alibaba Cloud: Qwen3.8-27B base model.
• ArmanJR and Decision Index authors: evaluation protocols and tools, obtained separately.
• Bev / Reza Sayar: serving integration, typed API, packaging and evaluation, with OpenAI Codex assistance.

This independent bundle does not imply affiliation or endorsement. The original Prism Apache-2.0 LICENSE and NOTICE are preserved with the weights; the source bundle includes all code notices.