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

TL;DR

KDFlow is a novel knowledge distillation framework for large language models that improves training efficiency and flexibility by decoupling architecture components, reducing communication costs, and supporting various distillation methods.

Contribution

It introduces a decoupled architecture with SGLang for inference, enabling efficient, flexible, and scalable LLM distillation with minimal engineering effort.

Findings

Achieves 1.44× to 6.36× speedup over existing KD frameworks.

Supports both off-policy and on-policy distillation methods.

Provides highly extensible APIs for cross-tokenizer KD.

Abstract

Knowledge distillation (KD) is an essential technique to compress large language models (LLMs) into smaller ones. However, despite the distinct roles of the student model and the teacher model in KD, most existing frameworks still use a homogeneous training backend (e.g., FSDP and DeepSpeed) for both models, leading to suboptimal training efficiency. In this paper, we present a novel framework for LLM distillation, termed \textbf{KDFlow}, which features a decoupled architecture and employs SGLang for teacher inference. By bridging the training efficiency of FSDP2 and the inference efficiency of SGLang, KDFlow achieves full utilization of both advantages in a unified system. Moreover, instead of transferring full logits across different processes, our framework only transmits the teacher's hidden states using zero-copy data transfer and recomputes the logits on the student side, effectively balancing the communication cost and KD performance. Furthermore, our framework supports both off-policy and on-policy distillation and incorporates KD algorithms for cross-tokenizer KD through highly extensible and user-friendly APIs. Experiments show that KDFlow can achieve \textbf{1.44$\times$ to 6.36$\times$} speedup compared to current KD frameworks, enabling researchers to rapidly prototype and scale LLM distillation with minimal engineering overhead. Code is available at: https://github.com/songmzhang/KDFlow