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.
FrontiersMind's decision model on a base it pre-trained from scratch. Each question reads the state and its own tokens once and a 256-dimension pointer head scores the options; Choice and Score take up to 255 entries, and nothing is generated.
Decides
choice, score, noul, rank, classify, route
choice, score, noul
Architecture
clm
lumma-fev
Fine-tuned from
qwen/qwen3-8b
frontiersmind/lumma-0.6b-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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64.0%
Calibration error
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Valid action rate
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Median latency
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45.8 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 lumma-fev?
clm is from Contrastive-LM and lumma-fev from FrontiersMind. Both have open weights you can download and run. Both answer choice, score and noul questions. Only clm answers rank, classify and route. lumma-fev is the smaller model, at 649M parameters to 8.0B.
Which is more accurate, clm or lumma-fev?
Only lumma-fev publishes an accuracy figure (64.0% on typed-decisions (maker's table; split not stated)); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or lumma-fev?
clm: Free (open weights). lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or lumma-fev locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull frontiersmind/lumma-fev download the weights.
