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.
Small open typed-decision model with calibrated probabilities, first-class Nepali coverage.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
gutsy
mira
Fine-tuned from
qwen/qwen3.5-0.8b
jhu-clsp/mmbert-small
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%
61.3%
Calibration error
—
0.083
Valid action rate
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100.0%
Median latency
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30 ms
p95 latency
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43 ms
Evaluation suite
JevBench public set (231 items; 169 correct), the maker's own run with Q8_0
s1-decision-bench
Latest version
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 mira?
gutsy is from Omar (kouhxp) and mira from SAGEA. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions.
Which is more accurate, gutsy or mira?
They report on different suites — gutsy 73.2% on JevBench public set (231 items; 169 correct), the maker's own run with Q8_0, mira 61.3% on s1-decision-bench — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gutsy or mira?
gutsy: Free (open weights). mira: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run gutsy or mira locally?
Yes, both: systemone pull kouhxp/gutsy and systemone pull sagea/mira download the weights.
