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.
SimpleJev's LoRA adapters and pointer head that turn an open model into a typed-decision model: given a state, a question and the options, it returns a probability for every option without generating text. This entry is the Qwen3.8-27B release; the others are linked.
Decides
choice, score, noul, rank, classify, route
choice, score, noul
Architecture
clm
jevany
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.8-27b
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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86.0%
Calibration error
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0.026
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 clm and jevany?
clm is from Contrastive-LM and jevany from SimpleJev. Both have open weights you can download and run. Both answer choice, score and noul questions. Only clm answers rank, classify and route. clm is the smaller model, at 8.0B parameters to 27B.
Which is more accurate, clm or jevany?
Only jevany publishes an accuracy figure (86.0% on SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own)); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or jevany?
clm: Free (open weights). jevany: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or jevany locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull simplejev/jevany download the weights.
Evaluation suite
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SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own)
