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README.md

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-The LLMQ Family
-
-From ETH Zuich, Beihang University, The University of Hong Kong
-
 Welcome to the official Hugging Face organization for LLMQ. In this organization, you can find quantized models of LLM by cutting-edge quantization methods. 
-In order to access models here, please selected the suitable model for your personal use. 
-
+In order to access models here, please select the suitable model for your personal use. 
 
-Team LLMQ is dedicated to advancing the field of Artificial Intelligence with a focus on enhancing efficiency. Our primary research interests include quantiation, binarization, efficient learning, etc.
-We are committed to innovate and develop cutting-edge techniques that make AI more accessible and sustainable, minimizing computational costs and maximizing performance. Our interdisciplinary approach leverages global expertise to push the boundaries of efficient AI technologies.
 
+We are dedicated to advancing the field of Artificial Intelligence with a focus on enhancing efficiency. Our primary research interests include quantiation, binarization, efficient learning, etc.
+We are committed to innovating and developing cutting-edge techniques that make AI more accessible and sustainable, minimizing computational costs and maximizing performance. Our interdisciplinary approach leverages global expertise to push the boundaries of efficient AI technologies.
 
-Our team is organized by Dr.Qin, who is a Postdoctoral Researcher at the Center for Project-Based Learning (PBL), ETH Zürich, Switzerland. 
+Recent Works:
 
-Contact Information: qinhaotong@gmail.com
+[22.04.2024] How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study. Arxiv, 2024. [ArXiv]() [GitHub]()