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 projects
2:42 - Alpha explained: the scaling problem
4:33 - Recommended alpha values and stability
5:01 - Target modules in the attention block
7:18 - Summary and practical recommendations

Watch this video if you are fine-tuning large language models using LoRA or QLoRA and want to make informed, principled choices instead of blindly copying defaults.

This video is part of the LLM Engineering and Deployment Certification Program by Ready Tensor.

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Видео LoRA Hyperparameters Explained: Choosing Rank, Alpha, and Target Modules канала Ready Tensor