unfork

unfork is the home of the AMOS decision-model family — generative decision models that turn a single forward pass into a normalized probability distribution, served through the systemone structured decision protocol.

What we build

Instead of free-form chat answers, AMOS models emit fully structured decision primitives:

Primitive	Output
`choice`	A probability distribution over candidate options
`score`	Calibrated numeric scores for each candidate
`noul`	An explicit "no usable label" distribution for out-of-scope inputs

This design yields 100% parseable structured output, calibrated probabilities, and low end-to-end latency — purpose-built for routing, classification, triage, and automated decision pipelines.

Flagship release

unfork/amos-2.8 — a QLoRA adapter on Qwen3-8B, trained on 50,567 curriculum-based decision tasks.

Highlights from independent randomized blind tests (rule-based gold scoring, zero text-level overlap with training data):

500-question fresh blind board: 96.6% raw accuracy (amos-2.8) vs 96.4% for jev-1.13 (commercial API) and 42.8% for laya (zero-shot); 98.6% on pure decision tasks; 96.05% under strict out-of-distribution evaluation.
6,000-question three-model same board: 86.77% on main tasks vs 72.28% (jev-1.13) and 45.07% (laya); 96.23% on out-of-scope attributes vs 86.87% and 43.89%.
0 API errors across all boards; ≈0.9–1.2 s warm end-to-end latency through the public gateway.

unfork

What we build

Flagship release

Links