Unsloth releases open-source guide to training a Jev-style decision model locally
Fine-tuning Qwen3.5 0.8B with Unsloth and LoRA on 4GB of VRAM raised aggregate accuracy across three decision benchmarks from 20.7% to 74.3%, with a GitHub repo and notebooks provided.

You can now train your own Decision model like Jev locally! We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM. Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo. We fine-tuned with a Clef head using Unsloth and LoRA (r=64) for one epoch, increasing downstream accuracy from 30–37% to 78%. GitHub: https://github.com/unslothai/unsloth Guide and Notebooks: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth