A Beginner's Guide to Fine Tune Large Language Model

This course offers a practical, beginner‑friendly introduction to fine‑tuning Large Language Models (LLMs). You’ll start by understanding what fine‑tuning is, why it matters, and where it’s used across real‑world applications. The course then walks through the major methods and techniques of fine‑tuning, with a special focus on LoRA(Low-Rank Adaptation). You will discover how these parameter‑efficient approaches make training large models accessible even on limited, single-consumer hardware—meaning you don't need a massive high-end compute cluster to build powerful, specialized AI.
Explore how fine-tuning is applied across industries, with hands-on focus on specialized tasks like medical domain question-answering.
Using GPT2 as the base model, you’ll explore its architecture, capabilities, and key statistics before diving into hands‑on training. You’ll learn how to prepare datasets, choose the right number of samples, configure training arguments, and run the full training loop using the Hugging Face's Trainer API. The course also covers saving, loading, and merging LoRA adapters back into the base model.

Finally, you’ll evaluate your fine‑tuned model against the original pretrained model to understand performance gains, supported by step‑by‑step code walkthroughs that make the entire process clear and repeatable. The course also add two additional use cases on classification and Named Entity recognition using different base models.