QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts

12 June 2024

Papers citing "QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts"

5 / 5 papers shown

Title
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness Zihao Zheng Xiuping Cui Size Zheng Maoliang Li Jiayu Chen Yun Liang Xiang Chen MQ MoE 64 0 0 27 Mar 2025
Foot-In-The-Door: A Multi-turn Jailbreak for LLMs Zixuan Weng Xiaolong Jin Jinyuan Jia Xinsong Zhang AAML 169 0 0 27 Feb 2025
Scaling Laws for Fine-Grained Mixture of Experts Jakub Krajewski Jan Ludziejewski Kamil Adamczewski Maciej Pióro Michal Krutul ... Krystian Król Tomasz Odrzygó'zd'z Piotr Sankowski Marek Cygan Sebastian Jaszczur MoE 51 54 0 12 Feb 2024
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 264 4,489 0 23 Jan 2020
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 245 1,826 0 17 Sep 2019