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.
Small open typed-decision model with calibrated probabilities, first-class Nepali coverage.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
jevany
mira
Fine-tuned from
qwen/qwen3.8-27b
jhu-clsp/mmbert-small
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%
61.3%
Calibration error
0.026
0.083
Valid action rate
—
100.0%
Median latency
—
30 ms
p95 latency
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43 ms
Evaluation suite
SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own)
s1-decision-bench
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 mira?
jevany is from SimpleJev and mira from SAGEA. Both have open weights you can download and run. Both answer choice, score and noul questions. Only mira answers classify and route.
Which is more accurate, jevany or mira?
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), mira 61.3% on s1-decision-bench — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jevany or mira?
jevany: Free (open weights). mira: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jevany or mira locally?
Yes, both: systemone pull simplejev/jevany and systemone pull sagea/mira download the weights.
