LoRA and PEFT: Efficient Fine-Tuning Techniques
LoRA and PEFT: Efficient Fine-Tuning Techniques
Full fine-tuning requires updating billions of parameters. LoRA introduces a smarter alternative.
1) What is LoRA?
Low-Rank Adaptation freezes original weights and trains small rank matrices instead.
2) Why It Matters
- Lower memory usage
- Faster training
- Reduced infrastructure cost
3) PEFT (Parameter Efficient Fine-Tuning)
LoRA is part of the broader PEFT family. These techniques modify only a subset of parameters.
4) Enterprise Advantage
Companies can fine-tune large models without owning massive GPU clusters.
5) Summary
LoRA makes model customization practical and scalable.
