How would you perform rank tuning in QLoRA for a large language model

Question

With the help of proper code can I know How would you perform rank tuning in QLoRA for a large language model?

Answer 1

You can perform rank tuning in QLoRA for a large language model by sweeping over different r values in LoraConfig and evaluating model performance on a validation set.

Here is the code snippet you can refer to:

In the above code we are using the following key strategies:

• Dynamically tests multiple ranks (r) to optimize parameter efficiency.

• Evaluates validation loss for performance comparison.

• Enables trade-off tuning between compute cost and accuracy.

Hence, rank tuning in QLoRA allows strategic balancing of adaptation capacity and resource usage, optimizing large model fine-tuning across different deployment needs.