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We introduce new methods for 1) accelerating and 2) stabilizing training for large language-vision models.
Mixed precision training with 8-bit floating point
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Pytorch: An imperative style, high-performance deep learning library
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, et al · 2005
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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Deep residual learning for image recognition
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Attention is all you need
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Trained ternary quantization
Chenzhuo Zhu, Song Han, Huizi Mao, and William J. Dally · 2017
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Training dnns with hybrid block floating point
Mario Drumond, Tao Lin, Martin Jaggi, and Babak Falsafi · 2018
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Noam Shazeer and Mitchell Stern · 2018
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Training deep neural networks with 8-bit floating point numbers
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan · 2018
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Training deep neural networks with 8-bit floating point numbers
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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On the convergence of adam and beyond
Sashank J Reddi, Satyen Kale, and Sanjiv Kumar · 2019
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Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks
Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang, and Kailash Gopalakrishnan · 2019
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Triton: an intermediate language and compiler for tiled neural network computations
Philippe Tillet, Hsiang-Tsung Kung, and David Cox · 2019
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Shibo Wang and Pankaj Kanwar · 2019
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Fixup initialization: Residual learning without normalization
Hongyi Zhang, Yann N Dauphin, and Tengyu Ma · 2019
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Shifted and squeezed 8-bit floating point format for low-precision training of deep neural networks
Léopold Cambier, Anahita Bhiwandiwalla, Ting Gong, Oguz H. Elibol, Mehran Nekuii, and Hanlin Tang · 2020
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Training with quantization noise for extreme model compression
Angela Fan, Pierre Stock, Benjamin Graham, Edouard Grave, Rémi Gribonval, Herve Jegou, and Armand Joulin · 2020
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Adaptive gradient methods at the edge of stability
Jeremy M Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, David Cardoze, Zachary Nado, George E Dahl, et al · 2022
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The case for 4-bit precision: k-bit inference scaling laws
Tim Dettmers and Luke Zettlemoyer · 2022
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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Scaling language-image pre-training via masking
Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He · 2022
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Glm-130b: An open bilingual pre-trained model
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