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AI & Machine Learning · Intermediate

Low-Rank Adaptation (LoRA)

A PEFT method that freezes base weights and trains small low-rank matrices added to selected layers.

Definition

LoRA approximates the weight update as the product of two small matrices of rank r, training often well under 1% of parameters. Adapters can be merged into the base weights for zero added latency or swapped dynamically to serve many customizations on one base model. Key hyperparameters are rank, alpha and target modules.

Exam tip

LoRA = low memory, small adapter files, frozen base model; higher rank = more capacity.

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