8 citations · 12 across the 12 of their papers we have counts for
4 papers · 1 filter
PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization
Mengzhao Chen, Yi Liu, Jiahao Wang +3
Existing weight-activation quantization methods for Large Language Models (LLMs) primarily address channel-wise outliers but often neglect token-wise outliers, which limits the acc…
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
Mengzhao Chen, Wenqi Shao, Peng Xu +4
Large language models (LLMs) are crucial in modern natural language processing and artificial intelligence. However, they face challenges in managing their significant memory requi…
Adapting LLaMA Decoder to Vision Transformer
Jiahao Wang, Wenqi Shao, Mengzhao Chen +7
This work examines whether decoder-only Transformers such as LLaMA, which were originally designed for large language models (LLMs), can be adapted to the computer vision field. We…
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
Peng Xu, Wenqi Shao, Mengzhao Chen +6
Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc. While their performance is im…