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Introduction to LoRA

Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) technique for fine-tuning large language models (LLMs).

How LoRA Works

LoRA freezes the pre-trained model weights and injects trainable rank-decomposition matrices into each layer of the Transformer architecture.

  1. Freeze: Keep the original pre-trained LLM weights.
  2. Inject: Add small, trainable “adapter” layers with fewer parameters.
  3. Train: Update only the adapter weights, significantly reducing training time.

Why Use LoRA?

  • Low VRAM: Fine-tune models on consumer-grade hardware.
  • Portability: Fast switching between different fine-tuned models by swapping tiny adapter weight files.
  • No Training Loss: LoRA doesn’t suffer from catastrophic forgetting like traditional fine-tuning.

Applications of LoRA

  • Domain Adaptation: Fine-tuning LLMs for medical, legal, or code generation.
  • Style Injection: Adapting LLMs to a specific writing style or brand tone.
  • Personalized AI: Building individual-user specialized assistants.