Chapter 2: Data Preparation for Fine-Tuning

A model's performance is fundamentally dependent on the quality of the data used for its training. Before you can adjust a model's behavior through fine-tuning, you must first assemble a dataset that clearly and consistently represents the desired capability. This chapter provides a systematic guide to preparing high-quality data for specializing a large language model.

You will learn the complete workflow for creating a model-ready dataset. We will cover:

Sourcing and Structuring: Methods for finding suitable public or private data and structuring it into effective formats, such as instruction-following or conversational pairs $(x, y)$ .
Cleaning and Preprocessing: Practical techniques for removing noise, handling inconsistencies, and normalizing text to improve training stability and final model performance.
Tokenization: The process of converting text into the numerical format required by the model, including strategies for handling special tokens, padding, and truncation.

The chapter concludes with a hands-on exercise where you will apply these techniques to process a raw text source into a structured, tokenized dataset ready for fine-tuning.

Sections

2.1 Sourcing and Selecting High-Quality Datasets
2.2 Instruction-Based vs. Conversational Data Formats
2.3 Data Cleaning and Preprocessing Techniques
2.4 Creating and Structuring Custom Datasets
2.5 Tokenization for Fine-Tuning
2.6 Hands-on Practical: Building a Fine-Tuning Dataset