# 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.

Oossa · 2026-09-28 · https://oossa.com/en/small-open-models-make-fast-decisions-in-local-training-project

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.

## The facts

- The creator reports training the 0.8B model in about two hours and the 2B model in about 3.5 hours on one RTX PRO 6000 GPU.
- The 0.8B model reportedly made a decision in about 28 milliseconds on an M4 Max.

## Why it matters

Small models that return choices quickly could suit apps where speed and running locally matter more than complex reasoning.

## Sources & references

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)](https://www.reddit.com/r/LocalLLaMA/comments/1wspn24/trained_locally_ultrafast_08b2b_system_1_decision/) – Reddit r/LocalLLaMA, 2026-09-28

Last updated: 2026-09-28