Using LoRA for Fine-tuning

LoRA (Low-Rank Adaptation) enables efficient fine-tuning by freezing pretrained weights and injecting small trainable matrices into transformer layers. This reduces trainable parameters to ~0.1% of the original model while maintaining performance.

Table of Contents

• Core Concepts
• Basic Setup
• Configuration Parameters
• QLoRA (Quantized LoRA)
• Training Patterns
• Saving and Loading
• Merging Adapters
• Best Practices
• References

Core Concepts

How LoRA Works