An open Jev-style LoRA on Qwen3.5-9B from Bespoke Labs, trained on 2,676 contrastively curated examples to score the allowed answer tokens directly for enums, booleans and rubric levels. Recipe, data and a public benchmark suite are released with it.
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, noul, score, classify, route
choice, noul, score
Architecture
nimble
nanojev
Fine-tuned from
qwen/qwen3.5-9b
qwen/qwen3-0.6b
License
apache-2.0
mit
Availability
Open weights + hosted API
Open weights
Hosted by
Bespoke Labs
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Input price
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Decision accuracy
90.1%
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Calibration error
0.054
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Valid action rate
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Median latency
106 ms
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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 bespoke-nimble-9b and nanojev?
bespoke-nimble-9b is from Bespoke Labs and nanojev from TianyuCodings. bespoke-nimble-9b has open weights and a hosted API; nanojev has open weights you can download and run. Both answer choice, noul and score questions. Only bespoke-nimble-9b answers classify and route. nanojev is the smaller model, at 600M parameters to 9.0B. bespoke-nimble-9b is licensed apache-2.0; nanojev, mit.
Which is more accurate, bespoke-nimble-9b or nanojev?
Only bespoke-nimble-9b publishes an accuracy figure (90.1% on Bespoke held-out set (324 examples)); nanojev does not, so there is no comparison to make without your own test.
Which is cheaper, bespoke-nimble-9b or nanojev?
bespoke-nimble-9b: Hosted, price not published, or free to self-host. nanojev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run bespoke-nimble-9b or nanojev locally?
Yes, both: systemone pull bespoke-labs/bespoke-nimble-9b and systemone pull tianyu-codings/nanojev download the weights.
