Summary

This chapter covered essential aspects of LLM fine-tuning, both in theory and practice. We examined the instruction data pipeline and how to create high-quality datasets, from curation to augmentation. Each pipeline stage offers optimization opportunities, particularly in quality assessment, data generation, and enhancement. This flexible pipeline can be adapted to your use cases by selecting the most relevant stages and techniques.

We applied this framework to real-world data from Chapter 3, using an LLM to convert raw text into instruction-answer pairs. We then explored SFT techniques. This included an analysis of SFT’s advantages and limitations, methods for storing and parsing instruction datasets with chat templates, and an overview of three primary SFT techniques: full fine-tuning, LoRA, and QLoRA. We compared these methods based on their impact on memory usage, training efficiency, and output quality. The chapter concluded with a practical demonstration...