1 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch
Jan Pfister, Julia Wunderle, Andreas Hotho
We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to…
Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
Hao Feng, Boyuan Zhang, Fanjiang Ye +9
DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitate…
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation
Liang Luo, Buyun Zhang, Michael Tsang +11
We study a mismatch between the deep learning recommendation models' flat architecture, common distributed training paradigm and hierarchical data center topology. To address the a…
Wukong: Towards a Scaling Law for Large-Scale Recommendation
Buyun Zhang, Liang Luo, Yuxin Chen +12
Scaling laws play an instrumental role in the sustainable improvement in model quality. Unfortunately, recommendation models to date do not exhibit such laws similar to those obser…