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Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks.
Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
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Training deep nets with sublinear memory cost
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
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PipeDream: Generalized pipeline parallelism for DNN training. In SOSP
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FTPipeHD: A Fault-Tolerant Pipeline-Parallel Distributed Training Approach for Heterogeneous Edge Devices
Yuhao Chen, Qianqian Yang, Shibo He, Zhiguo Shi, Jiming Chen, and Mohsen Guizani. 2023 · 2023
Cited alongside, same era.
Melon: Breaking the memory wall for resource-efficient on-device machine learning. In Mobisys
Qipeng Wang, Mengwei Xu, Chao Jin, Xinran Dong, Jinliang Yuan, Xin Jin, Gang Huang, Yunxin Liu, and Xuanzhe Liu. 2022b
Cited in the paper.
Lightseq2: Accelerated training for transformer-based models on gpus. In SC
Xiaohui Wang, Yang Wei, Ying Xiong, Guyue Huang, Xian Qian, Yufei Ding, Mingxuan Wang, and Lei Li. 2022a
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OpenAI. 2023 · 2023
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Java Native Interface — Wikipedia, The Free Encyclopedia
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EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models
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