How would you use LoRA Low-Rank Adaptation to fine-tune a large model with limited computational resources

Question

Can i know How would you use LoRA (Low-Rank Adaptation) to fine-tune a large model with limited computational resources?

Answer 1

You can use LoRA (Low-Rank Adaptation) to fine-tune a large model efficiently by updating only small, low-rank matrices instead of the full model, reducing computational and memory overhead.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Uses LoRA to fine-tune only a few parameters, making training efficient
• Reduces GPU memory usage by updating small low-rank matrices
• Applies LoRA only to specific layers (query & value projections)
• Maintains the generalization ability of the base model

Hence, LoRA enables efficient fine-tuning of large models on limited resources by updating only a subset of trainable parameters.