Knowledge Distillation

Train compact "Student" networks to inherit the intelligence of massive "Teacher" models.

The Teacher-Student Paradigm

Knowledge distillation is the process of transferring the Dark Knowledge (probability distributions) from a complex model to a lighter architecture. The student doesn't just learn if an image is a "cat"; it learns how sure the teacher was that it wasn't a "dog".

Teacher

Massive architecture (e.g., GPT-4, Llama-70B, ViT-Huge) used to generate soft-labels for training.

Student

Ultra-light architecture (e.g., MobileNet, TinyLlama) that achieves 90% accuracy with 10% parameters.

Distillation Command

Provide both models and your training dataset to begin the neural transfer.

edge-ai distill --teacher teacher.pt --student student.pt --data ./train_set

This process usually requires 12-24 hours of GPU time for optimal results.