Fine-Tuning Techniques

Course overview

Comparison of fine‑tuning approaches for open‑source LLMs, detailing full fine‑tuning versus PEFT, deep dives into LoRA/QLoRA and the TRL SFTTrainer, and an examination of hyperparameters and common failure modes to monitor during tuning.

Clear contrast between full fine‑tuning and PEFT approaches, highlighting practical implications for open‑source models.

Deep dive into LoRA and QLoRA: underlying principles, implementation considerations and use cases for efficient parameter tuning.

Walkthrough of the TRL SFTTrainer workflow applied to supervised fine‑tuning with operational cautions.

Review of critical hyperparameters, their impact on training dynamics, and common failure modes to diagnose.