A training-free layer that turns an open LLM into a decision model. It reads typed choice, yes/no and score answers from one prefill, removes option-order bias with no labels and, from a few hundred labels, calibrates or fits a closed-form head. Weights stay untouched.
French medical decision models by Bofeng Huang: pass a patient message or clinical note, a question and candidate answers, and get one probability per answer from a single forward pass. Choice, score and noul. A research model, not a medical device.
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
anyjev
docto-decision
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.5-4b
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
77.1%
88.9%
Calibration error
0.034
0.079
Valid action rate
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Median latency
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47 ms
p95 latency
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Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between anyjev and docto-decision?
anyjev is from Nokia and docto-decision from Bofeng Huang. Both have open weights you can download and run. Both answer choice, score and noul questions. Only anyjev answers classify and route. docto-decision is the smaller model, at 4.7B parameters to 8.0B.
Which is more accurate, anyjev or docto-decision?
They report on different suites — anyjev 77.1% on LocalLLaMA/typed-decisions (2,000 held-out decisions), L2 with 300 labels per question, docto-decision 88.9% on Docto Decision Bench fr v0.1 (12 tasks, mostly silver labels; the maker's own benchmark) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, anyjev or docto-decision?
anyjev: Free (open weights). docto-decision: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run anyjev or docto-decision locally?
Yes, both: systemone pull nokia/anyjev and systemone pull bofeng-huang/docto-decision download the weights.
Evaluation suite
LocalLLaMA/typed-decisions (2,000 held-out decisions), L2 with 300 labels per question
Docto Decision Bench fr v0.1 (12 tasks, mostly silver labels; the maker's own benchmark)
