Large Language Models (LLMs) can have billions of parameters.
Fine-tuning them usually requires high-end GPUs and large memory.
Parameter-Efficient Fine-Tuning (PEFT) offers a solution to adapt such models using fewer resources.
What is LoRA?
LoRA (Low-Rank Adaptation) is a PEFT technique where instead of updating the full model,
we only train small, low-rank matrices inserted inside the model layers.
Why Does This Work?
Most weight matrices in large models have redundancy.
LoRA approximates updates using smaller matrices, reducing the number of trainable parameters.
Key Benefits
- Requires much less GPU memory
- Faster training
- Can store multiple task adapters without duplicating full models
Example Comparison
If a model has 10 billion parameters, traditional fine-tuning updates all 10B parameters.
LoRA may only train around 10-50 million parameters, making it extremely resource-efficient.
Where LoRA is Used
- Chatbot customization
- Domain-specific summarization
- Speech and vision language models
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