Research Project
Resource Efficient LLM Fine-tuning and Serving
Abstract
Large Language Models (LLMs) have achieved great success in many real-world applications. However, fine-tuning and serving LLMs require much memory and computing resources. This project aims to develop cutting-edge techniques to improve the resource efficiency of LLM fine-tuning and serving.
Project members
Zeyi WEN
Assistant Professor
Project Period
2023-Present
Research Area
Data-driven AI & Machine Learning、High-Performance Systems for Data Analytics
Keywords
Efficiency, Fine-tuning, Large Language Models, Model Serving