Qwen3.5-4B with a merged rank-16 LoRA distilled from 17,408 teacher questions and 30,000 public training rows, read out as a temperature-scaled softmax over answer-letter logits. Also in 9B, 2B and GGUF.
A Gemma 4 12B fine-tune for typed decisions that also chats and reads images. Ships as GGUF for llama.cpp, holds a 64K context on a 16 GB GPU, and serves /v1/systemone next to /v1/chat/completions.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
jevk5
winnow
Fine-tuned from
qwen/qwen3.5-4b
google/gemma-4-12b-it
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
78.4%
85.7%
Calibration error
0.035
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Valid action rate
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Median latency
13.2 ms
143 ms
p95 latency
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Evaluation suite
JevBench public hard tier
JevBench public subset (231 items), Q8_0
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 jevk5 and winnow?
jevk5 is from Alibi Serikbay and winnow from EldanRing. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. jevk5 is the smaller model, at 4.0B parameters to 12B.
Which is more accurate, jevk5 or winnow?
They report on different suites — jevk5 78.4% on JevBench public hard tier, winnow 85.7% on JevBench public subset (231 items), Q8_0 — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jevk5 or winnow?
jevk5: Free (open weights). winnow: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, jevk5 or winnow?
By their publishers’ figures, jevk5 answers in about 13.2 ms at the median and winnow in about 143 ms — measured on different hardware, so treat it as a rough guide.
Can I run jevk5 or winnow locally?
Yes, both: systemone pull alibi-serikbay/jevk5 and systemone pull eldanring/winnow download the weights.