SimpleJev's LoRA adapters and pointer head that turn an open model into a typed-decision model: given a state, a question and the options, it returns a probability for every option without generating text. This entry is the Qwen3.8-27B release; the others are linked.
An open-weights System One model from Convai Innovations. A fully fine-tuned ModernBERT-large encoder with a from-scratch decision head that scores one marker per option and answers every question in a single 33–39 ms pass. Runs on your own CPU or GPU.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
jevany
laya
Fine-tuned from
qwen/qwen3.8-27b
answerdotai/modernbert-large
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
86.0%
—
Calibration error
0.026
—
Valid action rate
—
—
Median latency
—
39.5 ms
p95 latency
—
—
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 jevany and laya?
jevany is from SimpleJev and laya from Convai Innovations. Both have open weights you can download and run. Both answer choice, score and noul questions. Only laya answers classify and route. laya is the smaller model, at 421M parameters to 27B.
Which is more accurate, jevany or laya?
Only jevany publishes an accuracy figure (86.0% on SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own)); laya does not, so there is no comparison to make without your own test.
Which is cheaper, jevany or laya?
jevany: Free (open weights). laya: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jevany or laya locally?
Yes, both: systemone pull simplejev/jevany and systemone pull convai-innovations/laya download the weights.
Evaluation suite
SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own)
