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.
AutoTrust's student of TypeSafe Jev 1.13. A frozen Qwen3.8-27B plus a 108.9M-parameter decision block trained on Jev's own output distributions; one set of weights answers typed questions in one pass (System 1) or generates text with the untouched base (System 2).
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
anyjev
blocks-of-experts
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.8-27b
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%
88.7%
Calibration error
0.034
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Valid action rate
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Median latency
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137 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 jev-27b?
anyjev is from Nokia and jev-27b from AutoTrust AI Lab. Both have open weights you can download and run. Both answer choice, score and noul questions. Only anyjev answers classify and route. anyjev is the smaller model, at 8.0B parameters to 27B.
Which is more accurate, anyjev or jev-27b?
They report on different suites — anyjev 77.1% on LocalLLaMA/typed-decisions (2,000 held-out decisions), L2 with 300 labels per question, jev-27b 88.7% on JevBench public set (231 items), family-macro score, maker's run — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, anyjev or jev-27b?
anyjev: Free (open weights). jev-27b: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run anyjev or jev-27b locally?
Yes, both: systemone pull nokia/anyjev and systemone pull autotrust-ai/jev-27b download the weights.
Evaluation suite
LocalLLaMA/typed-decisions (2,000 held-out decisions), L2 with 300 labels per question
JevBench public set (231 items), family-macro score, maker's run
