@inproceedings{wang-etal-2025-easydistill,
title = "{E}asy{D}istill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models",
author = "Wang, Chengyu and
Yan, Junbing and
Cai, Wenrui and
Yue, Yuanhao and
Huang, Jun",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.60/",
doi = "10.18653/v1/2025.emnlp-demos.60",
pages = "787--795",
ISBN = "979-8-89176-334-0",
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. The toolkit, together with source codes, all model checkpoints and datasets, is released at: https://github.com/modelscope/easydistill."
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<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. The toolkit, together with source codes, all model checkpoints and datasets, is released at: https://github.com/modelscope/easydistill.</abstract>
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%0 Conference Proceedings
%T EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models
%A Wang, Chengyu
%A Yan, Junbing
%A Cai, Wenrui
%A Yue, Yuanhao
%A Huang, Jun
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F wang-etal-2025-easydistill
%X 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. The toolkit, together with source codes, all model checkpoints and datasets, is released at: https://github.com/modelscope/easydistill.
%R 10.18653/v1/2025.emnlp-demos.60
%U https://aclanthology.org/2025.emnlp-demos.60/
%U https://doi.org/10.18653/v1/2025.emnlp-demos.60
%P 787-795
Markdown (Informal)
[EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models](https://aclanthology.org/2025.emnlp-demos.60/) (Wang et al., EMNLP 2025)
ACL