How Implement LoRA-based fine-tuning for a 7B-parameter model using PyTorch

Question

Can you tell me How Implement LoRA-based fine-tuning for a 7B-parameter model using PyTorch.

Answer 1

You can implement LoRA-based fine-tuning for a 7B-parameter model using PyTorch by injecting low-rank adapters into the attention layers and updating only those parameters during training.

Here is the code snippet below:

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

• LoRA is injected into linear layers within attention blocks by wrapping them with low-rank adapters.

• Original weights are frozen to reduce trainable parameter count.

• Scaling is used to balance low-rank updates with frozen base outputs.

Hence, LoRA fine-tuning enables efficient training of large models by updating only a small number of additional parameters.