Summary
This chapter explored preference alignment techniques for improving LLMs. It introduced the concept of preference datasets, explaining their structure and importance in capturing nuanced human preferences. We implemented our own custom preference data generation pipeline by comparing original and AI-generated text from real articles. This pipeline can be reused and customized based on your use case.
We also provided an overview of the evolution of RLHF, leading to the introduction of DPO as a simpler and more efficient alternative. Finally, we implemented DPO using the Unsloth library to fine-tune our TwinLlama-3.1-8B model from Chapter 5. Our step-by-step tutorial gave practical instructions for training the model, as well as highlighting key differences from SFT. The final model is available on the Hugging Face Hub.
In the next chapter, we will explore the crucial topic of LLM evaluation, addressing the challenges and current approaches in assessing LLM performance...
