How does parameter freezing improve the efficiency of Generative AI during fine-tuning

Question

With the help of proper programming  can you tell me How does parameter freezing improve the efficiency of Generative AI during fine-tuning?

Answer 1

Parameter freezing improves efficiency during fine-tuning by limiting updates to a subset of model parameters, reducing computational costs, and preserving pre-trained knowledge while focusing on task-specific layers.

Here is the code snippet you can refer to:

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

• Loading a Pre-trained Model: Uses a pre-trained model on general tasks.
• Parameter Freezing: All layers except the final layer are frozen (requires_grad = False).
• Efficiency: Reduces the number of trainable parameters, speeding up fine-tuning.

Hence, parameter freezing optimizes fine-tuning by focusing updates on task-relevant layers, improving computational efficiency, and preserving generalizable pre-trained features.