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.
A 396M ModernBERT-large cross-encoder that answers typed questions about a text or a JSON state: one or several options, scores, yes/no with "not stated", spans with evidence quotes, rankings and numeric bins, each with a confidence and an act-or-escalate signal.
Decides
choice, score, noul
choice, score, noul, rank, extract
Architecture
jevany
solvi
Fine-tuned from
qwen/qwen3.8-27b
answerdotai/modernbert-large
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
86.0%
59.4%
Calibration error
0.026
0.210
Valid action rate
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Median latency
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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 jevany and solvi?
jevany is from SimpleJev and solvi from solvi. Both have open weights you can download and run. Both answer choice, score and noul questions. Only solvi answers rank and extract. solvi is the smaller model, at 396M parameters to 27B.
Which is more accurate, jevany or solvi?
They report on different suites — jevany 86.0% on SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own), solvi 59.4% on Fast Decisions public dev split (100 rows x 17 domains, macro), the maker's own harness, zero-shot — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jevany or solvi?
jevany: Free (open weights). solvi: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jevany or solvi locally?
Yes, both: systemone pull simplejev/jevany and systemone pull solvi-ai/solvi download the weights.
Evaluation suite
SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own)
Fast Decisions public dev split (100 rows x 17 domains, macro), the maker's own harness, zero-shot
