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.
Typed decisions from Google's frozen Gemma-4-12B-it, with no fine-tuning. A small shim over unmodified vLLM letters the options, reads the model's own probability for each letter at one answer position and applies one calibration temperature; one output token per decision.
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
bosun
cygnet
Fine-tuned from
qwen/qwen3-1.7b
google/gemma-4-12b-it
License
apache-2.0
mit
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
84.9%
87.9%
Calibration error
0.050
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Valid action rate
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Median latency
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50 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 bosun and cygnet?
bosun is from Hanno Labs and cygnet from Blockbrain Labs. Both have open weights you can download and run. Both answer choice, score and noul questions. Only bosun answers classify and route. bosun is the smaller model, at 2.0B parameters to 12B. bosun is licensed apache-2.0; cygnet, mit.
Which is more accurate, bosun or cygnet?
They report on different suites — bosun 84.9% on DecisionBench (Hanno-Labs/decision-bench, 23,900 rows; seen task families), cygnet 87.9% on JevBench public set (231 items), JevBench CLI — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, bosun or cygnet?
bosun: Free (open weights). cygnet: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run bosun or cygnet locally?
Yes, both: systemone pull hanno-labs/bosun and systemone pull blockbrain-labs/cygnet download the weights.
Evaluation suite
DecisionBench (Hanno-Labs/decision-bench, 23,900 rows; seen task families)
