When should you use PEFT techniques instead of full model fine-tuning

Question

Can you tell me When should you use PEFT techniques instead of full model fine-tuning?

Answer 1

You can use PEFT techniques instead of full model fine-tuning when you want efficient adaptation of large models with limited compute and storage resources.

Here is the code snippet below:

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

• prepare_model_for_kbit_training enables low-bit precision training to reduce memory.

• LoraConfig defines LoRA-specific hyperparameters.

• get_peft_model wraps the base model with trainable PEFT adapters.

Hence, PEFT is ideal when customizing large models under resource constraints without retraining all parameters.