LoRA Hyperparameters Explained: Choosing Rank, Alpha, and Target Modules

In this video, we break down the three most important hyperparameters in LoRA fine-tuning and explain how to choose them in practice: rank (r), alpha, and target modules. Rather than just listing defaults, we connect each parameter back to memory constraints, training stability, and real-world fine-tuning goals so you understand why these values matter and how to reason about them for your own use cases. Timestamps: 0:00 - Overview of LoRA hyperparameters 0:18 - Rank (r): capacity vs memory trade-offs 1:17 - Why low-rank LoRA works surprisingly well 2:03 - Practical r values used in real pro…