Qwen3.5-0.8B turned into a decision model with optiq lora train --decision: a LoRA on the backbone and a new joint schema head that scores noul, choice and score questions in one pass. It runs on Apple Silicon through MLX and OptiQ (optiq serve exposes /v1/systemone).
Latency is one request per case with all five questions, on an Apple M3 Max.
This model was fitted on the benchmark's own train split and nothing else: 1,080 records. The test cases are disjoint from it, but they come from the same four workflows, so expect less on decisions of a different kind. The Clef models were trained on other data. Top-label calibration (ECE) is no better than Clef's.
Correction, 2026-10-11. This card first reported 0.512 accuracy. That was a bug: the adapter was saved with tensor names that mlx-lm could not place, so it loaded without its LoRA weights. The adapter file is fixed and the numbers above are from the fixed file.