Jev Decision Models

A Jev-style typed-decision baseline built on the frozen Qwen3.5-4B checkpoint that accepts a local image path directly, letting the vision-language model score declared options without generating text. Ships scoring modes, a WebGPU browser demo and benchmark results measured on an RTX 3090.

Apache-2.0 decision models for browser agents: a Qwen3.5-35B-A3B fine-tune that answers jev-ultrafast's per-step operation and target-element Choice questions in one forward pass behind a Jev-compatible /v1/systemone API. Includes training code, a serving stack and a 125-task real-website evaluation.

Open decision models from a self-improvement loop that publishes every experiment, failures included. They answer Noul, Choice and Score questions on the same POST /v1/systemone wire format as Jev, returning a calibrated probability per option in one forward pass; the 27B scores 65.64 on the Decision Index 0.3 set.

Open System One decision models (27B, 9B and 4B on Qwen chat bases, in bf16, GGUF and MLX) that answer Choice, Noul and Score questions with a probability for every option from one forward pass. The repo carries the trainer, data builders, evaluation harnesses and a record of every run.

An open benchmark runtime for document-grounded decision models: it scores Jev Noul, Choice and Score answers on pinned tasks and counts malformed probability distributions as misses so model comparisons stay reproducible.

Independent training toolkit and open weights for System One decision models in the same category as Jev: 17M, 68M, and 400M models answering Choice, Noul, Score, and reranking questions, with Python and ONNX inference and a documented generalization gap to Jev 1.13 on the project's S1MB benchmark.