QuantEase: Optimization-based Quantization for Language Models

5 September 2023

Papers citing "QuantEase: Optimization-based Quantization for Language Models"

8 / 8 papers shown

Title
Semantic Retention and Extreme Compression in LLMs: Can We Have Both? Stanislas Laborde Martin Cousseau Antoun Yaacoub Lionel Prevost MQ 18 0 0 12 May 2025
Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis J. Zhao M. Wang Miao Zhang Yuzhang Shang Xuebo Liu Yaowei Wang Min Zhang Liqiang Nie MQ 58 1 0 18 Feb 2025
Scaling Laws for Floating Point Quantization Training X. Sun Shuaipeng Li Ruobing Xie Weidong Han Kan Wu ... Yangyu Tao Zhanhui Kang C. Xu Di Wang Jie Jiang MQ AIFin 53 0 0 05 Jan 2025
Multilingual Brain Surgeon: Large Language Models Can be Compressed Leaving No Language Behind Hongchuan Zeng Hongshen Xu Lu Chen Kai Yu 47 5 0 06 Apr 2024
A Simple and Effective Pruning Approach for Large Language Models Mingjie Sun Zhuang Liu Anna Bair J. Zico Kolter 50 353 0 20 Jun 2023
ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation Z. Yao Xiaoxia Wu Cheng-rong Li Stephen Youn Yuxiong He MQ 63 57 0 15 Mar 2023
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 139 684 0 31 Jan 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,424 0 23 Jan 2020