Fine-Tuning Large Language Models with Hugging Face and DPO

Open-source large language models offer incredible power, but tailoring them to specific business needs or domain-specific tasks requires proper customization. Understanding how to adapt these models safely and efficiently is an essential skill in modern AI development. This text-based course guides you through the entire lifecycle of LLM fine-tuning, from initial data preparation to advanced alignment techniques. You will learn how to transition from generic base models to specialized, high-performing AI assistants that follow instructions reliably and align with human preferences. What you'll learn: Understand the core terminology of pre-training, instruction tuning, and alignment; Prepare and format high-quality instruction datasets for training language models; Apply parameter-efficient fine-tuning (PEFT) techniques including LoRA and QLoRA; Implement Direct Preference Optimization (DPO) to align models with human feedback; Explore Reinforcement Learning from Human Feedback (RLHF) using Proximal Policy Optimization (PPO); Evaluate fine-tuned models using standard industry metrics and benchmarks. The course starts with foundational definitions of model parameters and dataset formatting, then progresses through detailed code walkthroughs for parameter-efficient training, concluding with state-of-the-art alignment strategies. You will study clear, written explanations and step-by-step Hugging Face code configurations. This course is designed for software developers, data enthusiasts, and aspiring AI engineers who want to learn model customization from scratch. No prior experience with fine-tuning is required, though a basic understanding of Python is helpful. Start reading today to unlock the full potential of custom language models.