Unofficial NoulXP package of Convai Innovations' Laya (English) (Apache-2.0), fp16: checked against the model's own answers, none changed. Not made by Convai Innovations.
Juspay's open decision model. Qwen3.6-35B-A3B with a merged rank-16 LoRA, shipped with a pinned SGLang serving bundle that answers Choice, Score and Noul questions over text, images or a video through a TypeSafe-compatible /v1/systemone API.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
laya
xor
Fine-tuned from
convai-innovations/laya
qwen/qwen3.6-35b-a3b
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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90.0%
Calibration error
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0.073
Valid action rate
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Median latency
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69 ms
p95 latency
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162 ms
Evaluation suite
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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 laya-noulxp-gpu and xor?
laya-noulxp-gpu is from Convai Innovations and xor from Juspay. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. laya-noulxp-gpu is the smaller model, at 421M parameters to 35B.
Which is more accurate, laya-noulxp-gpu or xor?
Only xor publishes an accuracy figure (90.0% on JevBench public set (231 items), maker's self-run of Xor 1.2); laya-noulxp-gpu does not, so there is no comparison to make without your own test.
Which is cheaper, laya-noulxp-gpu or xor?
laya-noulxp-gpu: Free (open weights). xor: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run laya-noulxp-gpu or xor locally?
Yes, both: systemone pull convai-innovations/laya-noulxp-gpu and systemone pull juspay/xor download the weights.
JevBench public set (231 items), maker's self-run of Xor 1.2
