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portfolio
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projects
ZO2: Zeroth-Order Offloading
Fine-tuning 175B parameter LLMs with only 18GB GPU memory
publications
WiP: Towards Light Adaptation of Large Language Models For Personal Hardware
Published in Mobisys workshop 2024, 2024
This paper explores efficient adaptation approaches for running large language models on personal hardware with limited resources.
Recommended citation: Liangyu Wang, Junxiao Wang and Di Wang. (2024). "WiP: Towards Light Adaptation of Large Language Models For Personal Hardware." Mobisys workshop 2024.
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FlashDP: Memory-Efficient and High-Throughput DP-SGD Training for Large Language Models
Published in NeurIPS workshop 2024, 2024
This paper presents a memory-efficient and high-throughput approach for training large language models with differential privacy guarantees.
Recommended citation: Liangyu Wang, Junxiao Wang, Jie Ren, Zihang Xiang, David E. Keyes, and Di Wang. (2024). "FlashDP: Memory-Efficient and High-Throughput DP-SGD Training for Large Language Models." NeurIPS workshop 2024.
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ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory
Published in NeurIPS workshop, 2024; arXiv preprint, 2025
This paper presents a novel framework for efficient zeroth-order fine-tuning of extremely large language models with limited GPU memory.
Recommended citation: Liangyu Wang, Jie Ren, Hang Xu, Junxiao Wang, Huanyi Xie, David E. Keyes, and Di Wang. (2025). "ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory." arXiv preprint arXiv:2503.12668
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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