Jev Qwen

Jev-style medical QA implementation on Qwen3.5-4B that selects an answer from candidates in one forward pass instead of generating text. It reports 69.42% accuracy versus 67.41% for standard generation and a 10.37x speedup across 153,889 questions from nine benchmark sets.

Open recipe for a local Jev-style decision model: a Qwen3.5-9B LoRA that scores one allowed answer token per Choice, boolean or rubric Score question, with data pipeline, training config and eval harness. Reports 90.1% on its 324-example holdout against Jev's 93.2%.

An open recipe for a 27B multimodal decision model trained with full-weight SFT on 73,000 examples over one H200, serving choice, noul and score on /v1/systemone with per-checkpoint provenance and released calibration plots.

A Jev-style one-pass skill router for OmO agents: a frozen Qwen2.5-0.5B with a tiny option-attention head scores the whole skill catalog per prompt, and an extension shrinks the system prompt to the top-K skills, failing open.

A training-free runtime that turns dense Qwen3 or Qwen3.5 checkpoints into a Jev-style Noul, Choice and Score service, sharing one state prefill across isolated questions and reading each answer from candidate-token logits.

Jag, a Jev-inspired visual grounding model: a Qwen3.5-0.8B backbone plus a lightweight MLP predicts normalized cxcywh bounding boxes for an image and referring expression in a single forward pass, removing autoregressive coordinate generation from the base model.

Together AI's open recipe and weights for a Jev-inspired decision model fine-tuned on Qwen3.5-4B, with the full data pipeline, training config, and saved benchmark results.

A reproduction of Jev that turns any Qwen checkpoint into a decision model serving the same /v1/systemone schema (Choice, Score, Noul), with no training and no generated answer text.