118 citations · 407 across the 19 of their papers we have counts for
6 papers · 1 filter
Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers
Zhewei Yao, Xiaoxia Wu, Conglong Li +4
Large-scale transformer models have become the de-facto architectures for various machine learning applications, e.g., CV and NLP. However, those large models also introduce prohib…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding
Connor Holmes, Minjia Zhang, Yuxiong He +1
In recent years, large pre-trained Transformer networks have demonstrated dramatic improvements in many natural language understanding tasks. However, the huge size of these models…
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang +3
How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memor…
Extreme Compression for Pre-trained Transformers Made Simple and Efficient
Xiaoxia Wu, Zhewei Yao, Minjia Zhang +2
Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. However, to prese…
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
Minjia Zhang, Xiaodong Liu, Wenhan Wang +2
Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks. However, NLMs a…