An embedding model turned decision model. Qwen3-Embedding-4B with a merged LoRA, driven by the open JevEmbed toolkit, which scores Choice, Score and Noul questions from embedding similarity and returns a distribution without generating text.
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, score, noul, classify
choice, score, noul, classify, route
Architecture
jevembed
kev
Fine-tuned from
qwen/qwen3-embedding-4b
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
85.9%
83.8%
Calibration error
—
0.042
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 jevembed and kev?
jevembed is from HIT-TMG (Lychee Team) and kev from Jared Palmer. Both have open weights you can download and run. Both answer choice, score, noul and classify questions. Only kev answers route. kev reads up to 8K tokens of state, against 1K tokens for jevembed.
Which is more accurate, jevembed or kev?
They report on different suites — jevembed 85.9% on JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging, 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, jevembed or kev?
jevembed: Free (open weights). kev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jevembed or kev locally?
Yes, both: systemone pull hit-tmg/jevembed and systemone pull jared-palmer/kev download the weights.
Evaluation suite
JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging
