Train Your Own Decision Model with Unsloth
Fine-tune Qwen/Gemma into decision classifiers with calibrated probabilities.
Using Unsloth desktop to train a classifier model.
Unsloth’s guide to turning ordinary LLMs (Qwen, Gemma, Llama) into decision models like their Jev/Laya/Clef: the fine-tuned model reads input, scores the options you provide, and returns a choice with a calibrated probability instead of generating text. LoRA (rank 16, 4-bit, lr 2e-4, 1–2 epochs) lifts accuracy from roughly chance (30–37%) to 73–81% on decision benchmarks (typed-decisions, BANKING77, CLINC150), with test sets decontaminated against training. Runs through the Unsloth Desktop app’s Train tab (macOS/Windows/Linux) or code via FastDecisionModel, then serves the model under their Decision API. Modest VRAM: a 0.8B model trains in 4 GB in ~40 minutes.
Source: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth