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 independent Jev-shaped reproduction on DeBERTa-v3-large. One state and any number of choice, score and yes/no questions go in, and a calibrated distribution per question comes out of one forward pass. Public gold labels only.
Decides
choice, score, noul, classify
choice, score, noul, classify
Architecture
jevembed
open-jev
Fine-tuned from
qwen/qwen3-embedding-4b
microsoft/deberta-v3-large
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%
85.4%
Calibration error
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0.022
Valid action rate
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Median latency
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28 ms
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 open-jev-deberta-v3-large?
jevembed is from HIT-TMG (Lychee Team) and open-jev-deberta-v3-large from Kotoba Labs. Both have open weights you can download and run. Both answer choice, score, noul and classify questions. jevembed reads up to 1K tokens of state, against 512 tokens for open-jev-deberta-v3-large. open-jev-deberta-v3-large is the smaller model, at 434M parameters to 4.0B.
Which is more accurate, jevembed or open-jev-deberta-v3-large?
They report on different suites — jevembed 85.9% on JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging, open-jev-deberta-v3-large 85.4% on Kotoba held-out test, seen question types (banking77, SST-5, BoolQ) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jevembed or open-jev-deberta-v3-large?
jevembed: Free (open weights). open-jev-deberta-v3-large: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jevembed or open-jev-deberta-v3-large locally?
Yes, both: systemone pull hit-tmg/jevembed and systemone pull kotoba-labs/open-jev-deberta-v3-large download the weights.
Evaluation suite
JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging
Kotoba held-out test, seen question types (banking77, SST-5, BoolQ)
