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.
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
gutsy
lumma-fev
Fine-tuned from
qwen/qwen3.5-0.8b
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
73.2%
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 gutsy and lumma-fev?
gutsy is from Omar (kouhxp) and lumma-fev from FrontiersMind. Both have open weights you can download and run. Both answer choice, score and noul questions. Only gutsy answers classify and route. lumma-fev is the smaller model, at 649M parameters to 800M.
Which is more accurate, gutsy or lumma-fev?
They report on different suites — gutsy 73.2% on JevBench public set (231 items; 169 correct), the maker's own run with Q8_0, lumma-fev 64.0% on typed-decisions (maker's table; split not stated) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gutsy or lumma-fev?
gutsy: Free (open weights). lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run gutsy or lumma-fev locally?
Yes, both: systemone pull kouhxp/gutsy and systemone pull frontiersmind/lumma-fev download the weights.
Evaluation suite
JevBench public set (231 items; 169 correct), the maker's own run with Q8_0
