EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models

2025-05-27

AI Summary

This paper addresses the lack of unified, user-friendly, and systematic tooling for knowledge distillation of large language models (LLMs) in both black-box and white-box settings. To this end, we propose EasyDistill—a full-stack distillation toolkit. Methodologically, it introduces: (1) the first distillation framework compatible with both System 1 (fast, intuitive) and System 2 (slow, deliberative) cognitive paradigms; (2) an integrated pipeline encompassing data synthesis, supervised fine-tuning, ranking optimization, and reinforcement learning; and (3) production-ready lightweight high-performance models, open-source datasets and code, and seamless integration with Alibaba Cloud’s PAI platform. Experimental results demonstrate that EasyDistill significantly reduces LLM deployment costs and improves inference efficiency. The toolkit has been validated in real-world industrial applications.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

In this paper, we present EasyDistill, a comprehensive toolkit designed for effective black-box and white-box knowledge distillation (KD) of large language models (LLMs). Our framework offers versatile functionalities, including data synthesis, supervised fine-tuning, ranking optimization, and reinforcement learning techniques specifically tailored for KD scenarios. The toolkit accommodates KD functionalities for both System 1 (fast, intuitive) and System 2 (slow, analytical) models. With its modular design and user-friendly interface, EasyDistill empowers researchers and industry practitioners to seamlessly experiment with and implement state-of-the-art KD strategies for LLMs. In addition, EasyDistill provides a series of robust distilled models and KD-based industrial solutions developed by us, along with the corresponding open-sourced datasets, catering to a variety of use cases. Furthermore, we describe the seamless integration of EasyDistill into Alibaba Cloud's Platform for AI (PAI). Overall, the EasyDistill toolkit makes advanced KD techniques for LLMs more accessible and impactful within the NLP community.

Problem

• Facilitates black-box and white-box knowledge distillation for LLMs
• Offers modular tools for data synthesis and model optimization
• Integrates with cloud platforms for scalable industrial solutions

Innovation

• Versatile toolkit for black-box and white-box KD
• Modular design with user-friendly interface
• Integration with Alibaba Cloud's PAI platform