This tutorial by Professor Hale explores how to fine-tune Large Language Models (LLMs) locally using the Python framework Axolotl. It covers setting up the development environment with `UV`, creating a custom `JSONL` dataset in `alpaca` format, configuring `Axolotl` via `YAML` to fine-tune a model with LoRA, and performing inference through both command-line and Python scripts. The process emphasizes parameter-efficient fine-tuning (PEFT) to adapt models for specific tasks, such as a custom string manipulation operation, showcasing how to tailor general intelligence to specialized applications on local hardware.