1 citations · 1 across the 4 of their papers we have counts for
7 papers
MTGR: Industrial-Scale Generative Recommendation Framework in Meituan
Ruidong Han, Bin Yin, Shangyu Chen +12
Scaling law has been extensively validated in many domains such as natural language processing and computer vision. In the recommendation system, recent work has adopted generative…
MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan
Yuxiang Wang, Chi Ma, Xiao Yan +15
Recommendation is crucial for both user experience and company revenue in Meituan as a leading lifestyle company, and generative recommendation models (GRMs) are shown to produce q…
First Activations Matter: Training-Free Methods for Dynamic Activation in Large Language Models
Chi Ma, Mincong Huang, Ying Zhang +5
Dynamic activation (DA) techniques, such as DejaVu and MoEfication, have demonstrated their potential to significantly enhance the inference efficiency of large language models (LL…
QQQ: Quality Quattuor-Bit Quantization for Large Language Models
Ying Zhang, Peng Zhang, Mincong Huang +7
Quantization is a proven effective method for compressing large language models. Although popular techniques like W8A8 and W4A16 effectively maintain model performance, they often…
MOYU: A Theoretical Study on Massive Over-activation Yielded Uplifts in LLMs
Chi Ma, Mincong Huang, Chao Wang +2
Massive Over-activation Yielded Uplifts(MOYU) is an inherent property of large language models, and dynamic activation(DA) based on the MOYU property is a clever yet under-explored…
Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study
Chi Ma, Mincong Huang, Chao Wang +2
In this work, we systematically investigate the efficacy of dynamic activation mechanisms within the LLaMA family of language models. Despite the potential of dynamic activation me…