54 citations · 142 across the 11 of their papers we have counts for
12 papers
BitNet b1.58 2B4T Technical Report
Shuming Ma, Hongyu Wang, Shaohan Huang +5
We introduce BitNet b1.58 2B4T, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale. Trained on a corpus of 4 trillion tokens, the model…
BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs
Hongyu Wang, Shuming Ma, Furu Wei
Efficient deployment of 1-bit Large Language Models (LLMs) is hindered by activation outliers, which complicate quantization to low bit-widths. We introduce BitNet v2, a novel fram…
Bitnet.cpp: Efficient Edge Inference for Ternary LLMs
Jinheng Wang, Hansong Zhou, Ting Song +7
The advent of 1-bit large language models (LLMs), led by BitNet b1.58, has spurred interest in ternary LLMs. Despite this, research and practical applications focusing on efficient…
BitNet a4.8: 4-bit Activations for 1-bit LLMs
Hongyu Wang, Shuming Ma, Furu Wei
Recent research on the 1-bit Large Language Models (LLMs), such as BitNet b1.58, presents a promising direction for reducing the inference cost of LLMs while maintaining their perf…
1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs
Jinheng Wang, Hansong Zhou, Ting Song +5
Recent advances in 1-bit Large Language Models (LLMs), such as BitNet and BitNet b1.58, present a promising approach to enhancing the efficiency of LLMs in terms of speed and energ…
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Hongyu Wang, Shuming Ma, Ruiping Wang +1
We introduce, Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs). Q-Sparse enables full sparsity of activations in LLMs which can…
M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models
Hongyu Wang, Jiayu Xu, Senwei Xie +6
Multilingual capability is an essential aspect for large multimodal models, since they are usually deployed across various countries and languages. However, most existing benchmark…
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…
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…
TorchScale: Transformers at Scale
Shuming Ma, Hongyu Wang, Shaohan Huang +8
Large Transformers have achieved state-of-the-art performance across many tasks. Most open-source libraries on scaling Transformers focus on improving training or inference with be…
Foundation Transformers
Hongyu Wang, Shuming Ma, Shaohan Huang +12
A big convergence of model architectures across language, vision, speech, and multimodal is emerging. However, under the same name "Transformers", the above areas use different imp…
DeepNet: Scaling Transformers to 1,000 Layers
Hongyu Wang, Shuming Ma, Li Dong +3
In this paper, we propose a simple yet effective method to stabilize extremely deep Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify the r…