With a foundation in using synthetic data for LLM pretraining, we now shift our attention to fine-tuning. This stage adapts general-purpose LLMs for specific tasks, improved instruction adherence, or distinct operational behaviors. Synthetic data provides a valuable resource for creating the targeted datasets required for effective fine-tuning, particularly when real-world data for specialized needs is insufficient or unavailable.

This chapter covers how to:

• Employ synthetically generated data for instruction fine-tuning (IFT), enabling LLMs to better understand and follow directives.
• Implement methods like Self-Instruct to construct diverse fine-tuning datasets.
• Produce synthetic examples to aid LLM performance in few-shot or zero-shot learning situations.
• Structure synthetic data in suitable formats (e.g., JSONL) for various fine-tuning pipelines.
• Utilize synthetic inputs to influence model characteristics, such as writing style or persona.
• Engage in a practical exercise by developing a synthetic dataset tailored for a specific fine-tuning goal.