LLM-420: Fine-Tuning and Parameter-Efficient Adaptation

Course Description

Most practitioners never train a model from scratch — but almost everyone fine-tunes. This course covers the spectrum of adaptation methods from full fine-tuning through highly parameter-efficient approaches, with emphasis on practical tradeoffs: compute cost, memory, downstream performance, and catastrophic forgetting. Students fine-tune models ranging from 7B to 70B parameters using LoRA and QLoRA.