The most-downloaded open System One reproduction. Merged Qwen3.5 fine-tunes, trained on about 95 public decision sets, then calibration-aware RL and a rank-64 LoRA, that softmax letter logits at an answer slot.
A 0.6B replica of the System One idea trained on game environments. A Qwen3-0.6B backbone with an attention-based Choice head that scores a dynamic candidate set for Maze, Snake, ViZDoom and position prediction, shipped with its full training pipeline.
Decides
choice, score, noul, classify, route
choice, noul, score
Architecture
decider
nanojev
Fine-tuned from
qwen/qwen3.5-2b-base
qwen/qwen3-0.6b
License
apache-2.0
mit
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
80.2%
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Calibration error
0.038
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Valid action rate
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Median latency
3.2 ms
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p95 latency
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Evaluation suite
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 decider and nanojev?
decider is from Mapika and nanojev from TianyuCodings. Both have open weights you can download and run. Both answer choice, score and noul questions. Only decider answers classify and route. nanojev is the smaller model, at 600M parameters to 2.0B. decider is licensed apache-2.0; nanojev, mit.
Which is more accurate, decider or nanojev?
Only decider publishes an accuracy figure (80.2% on Decider 67-task regression set); nanojev does not, so there is no comparison to make without your own test.
Which is cheaper, decider or nanojev?
decider: Free (open weights). nanojev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decider or nanojev locally?
Yes, both: systemone pull mapika/decider and systemone pull tianyu-codings/nanojev download the weights.
