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.
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
choice, score, noul, classify, route
Architecture
jevembed
winnow
Fine-tuned from
qwen/qwen3-embedding-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
85.9%
85.7%
Calibration error
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Valid action rate
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Median latency
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143 ms
p95 latency
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Evaluation suite
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 winnow?
jevembed is from HIT-TMG (Lychee Team) and winnow from EldanRing. Both have open weights you can download and run. Both answer choice, score, noul and classify questions. Only winnow answers route. winnow reads up to 64K tokens of state, against 1K tokens for jevembed. jevembed is the smaller model, at 4.0B parameters to 12B.
Which is more accurate, jevembed or winnow?
They report on different suites — jevembed 85.9% on JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging, 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, jevembed or winnow?
jevembed: Free (open weights). winnow: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jevembed or winnow locally?
Yes, both: systemone pull hit-tmg/jevembed and systemone pull eldanring/winnow download the weights.
JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging
