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.
A 144M-parameter System One model on the multilingual mmBERT-small encoder. Takes a state, a question and 2 to 20 options and returns one decision with probabilities for choice, score and yes/no questions, on a CPU.
Decides
choice, score, noul, rank, classify, route
choice, score, noul, classify, route
Architecture
clm
julia
Fine-tuned from
qwen/qwen3-8b
jhu-clsp/mmbert-small
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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73.2%
Calibration error
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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 julia-1?
clm is from Contrastive-LM and julia-1 from Supersonic Labs. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. Only clm answers rank. julia-1 is the smaller model, at 144M parameters to 8.0B.
Which is more accurate, clm or julia-1?
Only julia-1 publishes an accuracy figure (73.2% on typed-decisions test set (400 cases, 2,000 questions)); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or julia-1?
clm: Free (open weights). julia-1: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or julia-1 locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull supersonic-labs/julia-1 download the weights.
Evaluation suite
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typed-decisions test set (400 cases, 2,000 questions)
