LoRA & QLoRA Visualization

Understanding Low-Rank Adaptation and Quantized Fine-tuning for Large Language Models

Pre-trained LLM

Start with a large language model pre-trained on vast amounts of data

Quantization (QLoRA)

Reduce model precision while maintaining performance

Low-Rank Adaptation

Train small rank decomposition matrices instead of full weights

Fine-tuned Model

Resulting model adapted to specific tasks with minimal parameters

Original Model

Large Parameter Space

Parameters: 100

QLoRA Benefits

  • 4-bit quantization reduces memory usage by up to 75%
  • Enables fine-tuning on consumer GPUs
  • Maintains model quality through double quantization

LoRA Architecture

  • Trains rank decomposition matrices instead of full weights
  • Typically uses rank 8-32 for optimal balance
  • Enables efficient task-specific adaptations