An open Jev-style decision model by Andy Zhang: a LoRA adapter on Gemma 4 26B-A4B (also on Gemma 4 12B) that reads a state and returns a temperature-calibrated probability for every option of a yes/no, choice or score question in one forward pass.
Ruoxi Qiu's decision adapter for a frozen Qwen3.8-27B: a 6.3M-parameter LoRA on the last four text layers plus a numerical prior. One shared state feeds isolated question branches; each Choice, Noul or Score question returns probabilities with no generated tokens.
Decides
choice, score, noul
choice, noul, score
Architecture
ajev
canopy-jev
Fine-tuned from
google/gemma-4-26b-a4b-it
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
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87.4%
Calibration error
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Valid action rate
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Median latency
49 ms
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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 ajev and canopy-jev?
ajev is from Andy Zhang and canopy-jev from Ruoxi Qiu. Both have open weights you can download and run. Both answer choice, score and noul questions. ajev is the smaller model, at 26B parameters to 27B.
Which is more accurate, ajev or canopy-jev?
Only canopy-jev publishes an accuracy figure (87.4% on JevBench public set (231 items; 202 correct), the maker's own fresh zero-shot run with the upstream scorer); ajev does not, so there is no comparison to make without your own test.
Which is cheaper, ajev or canopy-jev?
ajev: Free (open weights). canopy-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run ajev or canopy-jev locally?
Yes, both: systemone pull andy-zhang/ajev and systemone pull camellia86/canopy-jev download the weights.
Evaluation suite
Sequential requests, the maker's own measurement
JevBench public set (231 items; 202 correct), the maker's own fresh zero-shot run with the upstream scorer