Machine Learning Mastery0 Hot9 min
How to Train Your own Decision Model like Jev using Unsloth
Unsloth now supports training decision models from open-weight language models that score a fixed set of choices instead of generating text. This tutorial fine-tunes Qwen3.5-4B with 4-bit LoRA on 10 intents from the CLINC150 dataset using Google Colab and a Tesla T4 GPU. Validation accuracy rose from 18.5% before training to 100% after 60 training steps, taking about 37.7 minutes. Expected calibration error dropped from 0.0816 to 0.00314, and the model classified all 300 held-out test examples correctly, though this result is limited to the small 10-intent experiment.