Fine-Tuning LLMs: From Data to Deployment

Fine-tuning large language models has gone from a niche research activity to an essential skill for AI engineers. Whether you need a model that speaks your company's language, follows a specific output format, or handles domain-specific tasks with expert-level accuracy, fine-tuning is often the difference between a prototype and a production system. This course gives you the practical knowledge to go from raw data to a deployed, custom model.

Module I — Foundations and Strategy (Lessons 1-2): Before touching any code, you need to know when fine-tuning is the right tool. We start with a decision framework that compares prompting, RAG, and fine-tuning, then survey the landscape of techniques from full fine-tuning to parameter-efficient methods like LoRA, QLoRA, and DoRA. You will understand the tradeoffs in compute, memory, and quality before committing to a path.

Module II — Data and Training (Lessons 3-7): The core of any fine-tuning project lives in the data. We cover dataset engineering from collection to formatting, then dive deep into LoRA and QLoRA mechanics. You will train models using both Hugging Face's ecosystem (transformers, peft, trl) and Unsloth for faster, more memory-efficient runs. Every lesson includes complete, runnable code.

Module III — Evaluation, Export, and Production (Lessons 8-12): Training is only half the battle. We cover monitoring and debugging training runs, evaluating your model with task-specific metrics and LLM-as-judge approaches, merging and exporting LoRA weights to production formats, deploying with inference engines like vLLM and Ollama, and finally a complete capstone project where you fine-tune, evaluate, merge, convert, and deploy your own model end-to-end.