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 family of System One models. A LoRA adapter plus a pointer head on a frozen Qwen base returns a distribution per typed question in one forward pass, serves TypeSafe's /v1/systemone contract, and ships a fitted temperature with every checkpoint.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
gutsy
kev
Fine-tuned from
qwen/qwen3.5-0.8b
qwen/qwen3.5-4b-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%
83.8%
Calibration error
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0.042
Valid action rate
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Median latency
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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 kev?
gutsy is from Omar (kouhxp) and kev from Jared Palmer. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. gutsy is the smaller model, at 800M parameters to 4.0B.
Which is more accurate, gutsy or kev?
They report on different suites — gutsy 73.2% on JevBench public set (231 items; 169 correct), the maker's own run with Q8_0, kev 83.8% on transfer-v4 (locked, out of domain) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gutsy or kev?
gutsy: Free (open weights). kev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run gutsy or kev locally?
Yes, both: systemone pull kouhxp/gutsy and systemone pull jared-palmer/kev download the weights.
Evaluation suite
JevBench public set (231 items; 169 correct), the maker's own run with Q8_0
