In this article, you will learn how to turn an open-weight language model into a decision model that classifies user intent, by fine-tuning Qwen3.5-4B on the CLINC150 dataset with Unsloth.
What if you could turn a regular language model into a decision model that chooses an answer instead of generating a long response?
Models like Jev, Laya, and Clef are designed to make structured decisions. Instead of generating text token by token, they select from predefined options and return probability estimates.
Unsloth now supports training decision models from open-weight language models such as Qwen and Gemma. Its official decision-model training guide explains the method and provides ready-to-run notebooks.
Source: Machine Learning Mastery — Read the full article on the original site ↗