Introduction to LLM Fine-Tuning on Domain Data

As Large Language Models become central to modern applications, the ability to adapt them to specialized tasks is a highly sought-after skill. General models are powerful, but they often lack the specific context needed for niche industry domains. This course provides a written, step-by-step foundation in LLM fine-tuning, starting from basic concepts and terminology. You will explore how to take a base model and adapt it using custom domain data, focusing on dataset preparation and instruction tuning. By reading through practical examples and code snippets, you will understand the end-to-end pipeline required to make an LLM follow specialized instructions. What you will learn: - Understand core LLM terminology and the difference between pre-training and fine-tuning. - Prepare and format custom domain datasets for instruction tuning using modern conversational formats. - Apply modern Parameter-Efficient Fine-Tuning (PEFT) concepts like LoRA to understand how computational costs are reduced. - Configure training pipelines using current industry conventions. - Evaluate fine-tuned models to ensure they generate accurate, domain-specific responses. The curriculum flows from foundational AI concepts to the practical steps of formatting data, configuring training scripts, and evaluating results. You will read through detailed explanations and analyze code snippets that demonstrate how these concepts are implemented in real-world scenarios. This course is designed for beginners, aspiring ML engineers, and developers looking to transition into AI. No prior experience with model training is required, though basic programming literacy is helpful. Start reading today to build your foundational skills in adapting Large Language Models for custom tasks.