Omar (kouhxp)'s CPU decision model: a Qwen3.5-0.8B fine-tune shipped as GGUF for llama.cpp that answers yes/no, choice and score questions with a probability per option plus a 'none of these' reject probability, served by a local Jev-style HTTP runtime.
An open-weights System One model from Convai Innovations. A fully fine-tuned ModernBERT-large encoder with a from-scratch decision head that scores one marker per option and answers every question in a single 33–39 ms pass. Runs on your own CPU or GPU.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
gutsy
laya
Fine-tuned from
qwen/qwen3.5-0.8b
answerdotai/modernbert-large
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
73.2%
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Calibration error
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Valid action rate
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Median latency
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39.5 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 gutsy and laya?
gutsy is from Omar (kouhxp) and laya from Convai Innovations. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. gutsy reads up to 8K tokens of state, against 512 tokens for laya. laya is the smaller model, at 421M parameters to 800M.
Which is more accurate, gutsy or laya?
Only gutsy publishes an accuracy figure (73.2% on JevBench public set (231 items; 169 correct), the maker's own run with Q8_0); laya does not, so there is no comparison to make without your own test.
Which is cheaper, gutsy or laya?
gutsy: Free (open weights). laya: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run gutsy or laya locally?
Yes, both: systemone pull kouhxp/gutsy and systemone pull convai-innovations/laya download the weights.
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Evaluation suite
JevBench public set (231 items; 169 correct), the maker's own run with Q8_0
