How to Fine Tune Language Models Using DPO

Language models often pick up bad habits from their training data. We show you how to check for these biases in the Anthropic dataset. Specifically, we look for cases where the model prefers longer answers regardless of quality.

Our process uses tools like TRL and LoRA to keep things efficient. This setup lets you train models on consumer hardware without needing massive resources. We walk through the entire pipeline step by step.

The goal is to teach the model to learn actual user preferences. By following these steps, you can stop the model from taking shortcuts. Your final result will be a smarter and more reliable assistant.