Contrastive Language Models score a state against a set of candidate actions with a contrastive objective. Two projection heads on frozen Qwen3-8B embeddings, trained with InfoNCE; clm-serve maps Choice, Score and Noul onto candidate ranking.
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, rank, classify, route
choice, score, noul, classify
Architecture
clm
open-jev
Fine-tuned from
qwen/qwen3-8b
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
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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 clm and open-jev-deberta-v3-large?
clm is from Contrastive-LM 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. Only clm answers rank and route. open-jev-deberta-v3-large is the smaller model, at 434M parameters to 8.0B.
Which is more accurate, clm or open-jev-deberta-v3-large?
Only open-jev-deberta-v3-large publishes an accuracy figure (85.4% on Kotoba held-out test, seen question types (banking77, SST-5, BoolQ)); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or open-jev-deberta-v3-large?
clm: Free (open weights). open-jev-deberta-v3-large: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or open-jev-deberta-v3-large locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull kotoba-labs/open-jev-deberta-v3-large download the weights.
Evaluation suite
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Kotoba held-out test, seen question types (banking77, SST-5, BoolQ)
