KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models

Researchers have developed KDFlow, a new framework for compressing large language models that achieves 1.44x to 6.36x faster training speeds compared to existing knowledge distillation methods. The framework uses a decoupled architecture that optimizes both training and inference efficiency while reducing communication costs through innovative data transfer techniques.

• →KDFlow introduces a decoupled architecture that separates student and teacher model processing for optimal efficiency.
• →The framework achieves 1.44x to 6.36x speedup compared to current knowledge distillation frameworks.
• →It uses zero-copy data transfer for hidden states instead of full logits to reduce communication costs.
• →The system supports both off-policy and on-policy distillation with extensible APIs.
• →KDFlow enables rapid prototyping and scaling of LLM compression with minimal engineering overhead.