knowledge-distillation

Distill a large language model by transferring knowledge from a teacher to a smaller student using PyTorch and transformers, applying temperature scaling and soft targets to preserve performance while reducing compute overhead.

Reduce LLM inference costs on constrained hardware through teacher-student distillation, generating a smaller student model that maintains performance while requiring less memory and enabling faster, cost-effective deployment.

Knowledge distillation supports multi-teacher setups in PyTorch, allowing you to transfer knowledge from multiple teacher models simultaneously into a single student using transformers and datasets.

Use reverse KLD for model compression with transformers through the MiniLLM approach, which is supported alongside temperature scaling and two-stage distillation workflows to optimize the student model.

To run an end-to-end distillation pipeline, you need Python tooling with the transformers, torch, and datasets libraries installed to execute teacher-student and multi-teacher strategies.

Use model compression instead of training from scratch because distillation transfers knowledge from a powerful teacher to a smaller student, preserving performance while significantly reducing the compute and memory required for deployment.