Knowledge distillation – transferring wisdom efficiently
Knowledge distillation is an effective technique for model compression and optimization, particularly useful for deploying sophisticated models such as LLMs on devices with limited resources. The process involves the aspects covered next.
Teacher-student model paradigm
Let’s take a deeper dive into the concept of the teacher-student model paradigm in knowledge distillation:
- Teacher model: The “teacher” model serves as the source of knowledge in knowledge distillation. It is a well-established and usually complex neural network that has been extensively trained on a large dataset. This model has achieved high accuracy and is considered an expert in the task it was trained for. The teacher model serves as a reference or a benchmark for high-quality predictions.
- Student model: In contrast, the “student” model is a compact and simplified neural network with fewer parameters...
