What is LoRA (Low-Rank Adaptation) and why is it important for efficient LLM fine-tuning?

LoRA is a parameter-efficient fine-tuning technique that adapts large language models using low-rank trainable matrices instead of updating all model parameters.

Full fine-tuning of modern LLMs is extremely expensive because models may contain billions of parameters.

Challenges:

LoRA solves this by freezing original model weights and injecting small trainable low-rank matrices.

Core idea: Instead of updating full weight matrix W:

W' = W + ΔW

LoRA decomposes updates into: ΔW = A × B

Where:

Benefits:

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