Provide code to use knowledge distillation for model compression in a Generative model without losing performance

Question

Can you provide code to use knowledge distillation for model compression in a Generative model without losing performance?

Answer 1

To perform knowledge distillation for compressing a generative model, you train a smaller student model to mimic the outputs (e.g., logits, features) of a larger teacher model while using a combination of task-specific and distillation losses. Here is the code you can refer to: 

In the above code, we are using the following:

• Teacher-Student Architecture:

  • The teacher is a large, pre-trained model.
  • The student is a smaller, lightweight model.

• Loss Functions:

  • Task loss ensures the student achieves the original task's goal.
  • Distillation loss aligns the student with the teacher's learned knowledge.

• Temperature Scaling:

  • A higher temperature softens the logits for better gradient flow.

• Performance Retention:

  • By combining task-specific loss with distillation loss, the student retains performance close to the teacher's.

By referring to above you can use knowledge distillation for model compression in a Generative model without losing performance