This documentation introduces how to fine-tune large language models into decision models using Unsloth. These models are designed to make decisions with calibrated probabilities rather than simply generating text. Users can train models like Qwen or Gemma to read input, score provided options, and return a specific choice. The process is supported through the Unsloth Desktop app, free notebooks, or custom Python code, and the resulting model can be integrated directly into the Decision API for practical use.
