Unofficial NoulXP package of Convai Innovations' Laya (English) (Apache-2.0), fp32-o23: checked against the model's own answers, none changed. Not made by Convai Innovations.
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, classify, route
choice, score, noul
Architecture
laya
lumma-fev
Fine-tuned from
convai-innovations/laya
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
116 ms
45.8 ms
p95 latency
859 ms
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Evaluation suite
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 laya-noulxp-cpu and lumma-fev?
laya-noulxp-cpu is from Convai Innovations and lumma-fev from FrontiersMind. Both have open weights you can download and run. Both answer choice, score and noul questions. Only laya-noulxp-cpu answers classify and route. lumma-fev reads up to 8K tokens of state, against 512 tokens for laya-noulxp-cpu. laya-noulxp-cpu is the smaller model, at 421M parameters to 649M.
Which is more accurate, laya-noulxp-cpu or lumma-fev?
Only lumma-fev publishes an accuracy figure (64.0% on typed-decisions (maker's table; split not stated)); laya-noulxp-cpu does not, so there is no comparison to make without your own test.
Which is cheaper, laya-noulxp-cpu or lumma-fev?
laya-noulxp-cpu: Free (open weights). lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, laya-noulxp-cpu or lumma-fev?
By their publishers’ figures, lumma-fev answers in about 45.8 ms at the median and laya-noulxp-cpu in about 116 ms — measured on different hardware, so treat it as a rough guide.
Can I run laya-noulxp-cpu or lumma-fev locally?
Yes, both: systemone pull convai-innovations/laya-noulxp-cpu and systemone pull frontiersmind/lumma-fev download the weights.
Measured by System One Models on 2026-10-05: one question per request, 4 threads of a Xeon Gold 6342
