37 citations · 114 across the 14 of their papers we have counts for
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cs.CL2025
AttentionEngine: A Versatile Framework for Efficient Attention Mechanisms on Diverse Hardware Platforms
Feiyang Chen, Yu Cheng, Lei Wang +8
Transformers and large language models (LLMs) have revolutionized machine learning, with attention mechanisms at the core of their success. As the landscape of attention variants e…
cs.CL2024★ 37 cited
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Shuming Ma, Hongyu Wang, Lingxiao Ma +7
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in whic…
cs.CL2023★ 26 cited
BitNet: Scaling 1-bit Transformers for Large Language Models
Hongyu Wang, Shuming Ma, Li Dong +7
The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we int…