Unsloth releases free notebook to train decision models locally
Unsloth published a free Colab notebook and companion guide showing how to train Qwen3.5-4B locally into a “decision model” that outputs decisions instead of text, using only 8GB of VRAM. Unsloth says its open-source repo can turn any model, such as Qwen3.8 or Gemma 4, into a decision model, and claims it raised Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across three decision benchmarks on just 4GB of VRAM (these figures are Unsloth’s own and the benchmark methodology was not disclosed). The walkthrough covers data preparation (state, questions, gold answers), training and serving, and likens the result to Jev.
It lowers the barrier to turning small models into decision-making systems on consumer GPUs, making local experimentation easier.