A multimodal decision classifier over text, image, audio and video on Gemma 4 12B, fine-tuned on 30,000 questions. Returns a probability per option and generates nothing.
An open family of System One models. A LoRA adapter plus a pointer head on a frozen Qwen base returns a distribution per typed question in one forward pass, serves TypeSafe's /v1/systemone contract, and ships a fitted temperature with every checkpoint.
Decides
choice, noul, score, classify
choice, score, noul, classify, route
Architecture
jev-omni
kev
Fine-tuned from
google/gemma-4-12b-it
qwen/qwen3.5-4b-base
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
87.6%
83.8%
Calibration error
0.040
0.042
Valid action rate
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Median latency
83 ms
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p95 latency
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Evaluation suite
DecisionBench Medium (author's set)
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 jev-omni and kev?
jev-omni is from akhilaaa3 and kev from Jared Palmer. Both have open weights you can download and run. Both answer choice, noul, score and classify questions. Only kev answers route. kev is the smaller model, at 4.0B parameters to 12B.
Which is more accurate, jev-omni or kev?
They report on different suites — jev-omni 87.6% on DecisionBench Medium (author's set), kev 83.8% on transfer-v4 (locked, out of domain) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jev-omni or kev?
jev-omni: Free (open weights). kev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jev-omni or kev locally?
Yes, both: systemone pull akhilaaa3/jev-omni and systemone pull jared-palmer/kev download the weights.
