Hanno Labs' small calibrated decision model. A LoRA on Qwen3-1.7B plus trained decision-token embeddings with stable slots, returning the full distribution over up to 255 caller-defined choices and a null slot for Choice, Score and Noul questions.
Small open typed-decision model with calibrated probabilities, first-class Nepali coverage.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
bosun
mira
Fine-tuned from
qwen/qwen3-1.7b
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
84.9%
74.5%
Calibration error
0.050
0.025
Valid action rate
—
100.0%
Median latency
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30 ms
p95 latency
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43 ms
Evaluation suite
DecisionBench (Hanno-Labs/decision-bench, 23,900 rows; seen task families)
s1-decision-bench
Latest version
3.1.0
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 bosun and mira?
bosun is from Hanno Labs 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, bosun or mira?
They report on different suites — bosun 84.9% on DecisionBench (Hanno-Labs/decision-bench, 23,900 rows; seen task families), mira 74.5% on s1-decision-bench — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, bosun or mira?
bosun: Free (open weights). mira: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run bosun or mira locally?
Yes, both: systemone pull hanno-labs/bosun and systemone pull sagea/mira download the weights.
