LoRA & Parameter-Efficient Fine-Tuning
Master Low-Rank Adaptation and parameter-efficient fine-tuning techniques for large language models
50 min read•Advanced
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What is LoRA (Low-Rank Adaptation)?
LoRA is a parameter-efficient fine-tuning technique that freezes pretrained model weights and adds trainable low-rank decomposition matrices to each layer. Instead of updating billions of parameters, LoRA trains only a small fraction (0.1-1%) by learning rank decomposition matrices A and B, making fine-tuning affordable and accessible while maintaining comparable performance to full fine-tuning.
LoRA Efficiency Calculator
Efficiency Metrics
Trainable Parameters:17,134,797
Parameter Efficiency:0.24%
Memory Usage:18.3 GB
Training Time:5.2 hours
Estimated Cost:$21
Scaling Factor (α/r):2.0
Implementation Examples
LoRA Layer Implementation
PyTorch LoRA Layer
Complete Training Pipeline
LoRA Training with HuggingFace
QLoRA Implementation
4-bit QLoRA Training System
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