Data Preparation for Fine-tuning
The performance of a fine-tuned Large Language Model is fundamentally tied to the data it learns from during adaptation. This chapter provides techniques for constructing and formatting datasets tailored for instruction following and domain specialization.
You will learn the principles behind instruction tuning and practical approaches for sourcing, creating, and structuring effective instruction datasets. We will cover the specific formats required for Supervised Fine-tuning (SFT) and examine the data considerations unique to adapting models for specific domains. Additionally, we will address strategies for managing situations with limited or imbalanced data and introduce text data augmentation methods to enhance fine-tuning outcomes. The goal is to equip you with the skills to prepare high-quality data that effectively guides the LLM towards desired behaviors and capabilities.
Sections
2.1 Instruction Tuning Principles
2.2 Sourcing and Constructing Instruction Datasets
2.3 Formatting Data for Supervised Fine-tuning (SFT)
2.4 Domain Adaptation Data Requirements
2.5 Handling Data Scarcity and Imbalance
2.6 Data Augmentation Techniques for Text
2.7 Practice: Preparing an Instruction Tuning Dataset
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