Fine-tuning Qwen3.5 0.8B on 4GB VRAM in 10 minutes lifts decision accuracy 37%→65%
danielhanchen · x · 2026-10-10
A developer trained a local Jev-style decision model on Qwen3.5 0.8B: just 60 LoRA steps (10 minutes) on 4GB of VRAM boosted accuracy from 37% to 65%.
A full tutorial video covers setup in Unsloth Studio, model/dataset choice, LoRA training, in-app testing, the code workflow, evaluation, and inference deployment. An accompanying article explains the Jev thesis: simple decisions shouldn't hammer every problem with a large LLM—small models answer in milliseconds at a fraction of the cost.
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