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 4B decision model fine-tuned from Qwen3.5-4B-Base. A state and typed questions (choice, yes/no, score) go in; a temperature-calibrated probability for every option comes out of one pass, through a self-hosted /v1/systemone-compatible server. Apache-2.0 weights and code.
Decides
choice, score, noul, rank, classify, route
choice, score, noul, classify, route
Architecture
clm
wald
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.5-4b-base
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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88.3%
Calibration error
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0.045
Valid action rate
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Median latency
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33 ms
p95 latency
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168 ms
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 wald?
clm is from Contrastive-LM and wald from ORG2 AI. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. Only clm answers rank. wald is the smaller model, at 4.2B parameters to 8.0B.
Which is more accurate, clm or wald?
Only wald publishes an accuracy figure (88.3% on JevBench public set (231 items; 204 correct), maker's run with the JevBench CLI, v1.2, effort none; items used as a development scoreboard); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or wald?
clm: Free (open weights). wald: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or wald locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull org2ai/wald download the weights.
Evaluation suite
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JevBench public set (231 items; 204 correct), maker's run with the JevBench CLI, v1.2, effort none; items used as a development scoreboard
Latest version
2026.09.25
1.2.0
Variants
LICENSE
Dockerfile, LICENSE, NOTICE, model-files, reference, server
