Note · 1 min read
Small open models make fast decisions in local training project
A Reddit user has released small, open-weight models that choose among options and return probabilities without generating text. The user reports a 28-millisecond decision time for the 0.8B model and an 83.1% score for the 2B model on a five-benchmark test.
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The models, called Jeff, are fine-tunes of Qwen3.5 and Gemma for classification and other choices. Give one a situation and a list of options, and it returns a probability for each in a single pass.
The creator says they trained the models on local hardware and released the weights under the Apache 2.0 license. On a five-benchmark panel, the 2B model scored 83.1%, close to Jev’s published 83.0%; the tests used different samples, so the scores are not a direct comparison. Jeff is much weaker on multi-step reasoning, according to the post.
Why it matters
Small models that return choices quickly could suit apps where speed and running locally matter more than complex reasoning.
Sources & references
| # | Source | Outlet | Date | Key takeaway |
|---|---|---|---|---|
| 1 | Trained locally: ultra-fast 0.8B/2B System 1 decision models that match Jev on benchmarks and Doom, ~30 ms per decision (open weights) ↗ | Reddit r/LocalLLaMA | Sep 28, 2026 | TL;DR: The Jeff models are a set of Qwen3.5 and Gemma fine-tunes for zero-shot classification: small, efficient, open-weight models with res |
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