Fetching the paper…
Reading the bibliography…
Training Large Language Models (LLMs) is memory-intensive due to the large number of parameters and associated optimization states.
Numerical inverting of matrices of high order
John Von Neumann and Herman Heine Goldstine · 1947
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
Earlier work this paper cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
Earlier work this paper cites.
Fxpnet: Training a deep convolutional neural network in fixed-point representation
Xi Chen, Xiaolin Hu, Hucheng Zhou, and Ningyi Xu · 2017
Earlier work this paper cites.
Training quantized nets: A deeper understanding
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, and Tom Goldstein · 2017
Earlier work this paper cites.
Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
Earlier work this paper cites.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Earlier work this paper cites.
Training deep neural networks with 8-bit floating point numbers
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan · 2018
Earlier work this paper cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
Earlier work this paper cites.
Swalp: Stochastic weight averaging in low precision training
Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, and Chris De Sa · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Earlier work this paper cites.
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, et al · 2020
Earlier work this paper cites.
Ultra-low precision 4-bit training of deep neural networks
Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Viji Srinivasan, and Kailash Gopalakrishnan · 2020
Earlier work this paper cites.
Towards unified int8 training for convolutional neural network
Feng Zhu, Ruihao Gong, Fengwei Yu, Xianglong Liu, Yanfei Wang, Zhelong Li, Xiuqi Yang, and Junjie Yan · 2020
Earlier work this paper cites.
Training high-performance and large-scale deep neural networks with full 8-bit integers
Yukuan Yang, Lei Deng, Shuang Wu, Tianyi Yan, Yuan Xie, and Guoqi Li · 2020
Earlier work this paper cites.
Q-bert: Hessian based ultra low precision quantization of bert
Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Earlier work this paper cites.
8-bit optimizers via block-wise quantization
Tim Dettmers, Mike Lewis, Sam Shleifer, and Luke Zettlemoyer · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Dkm: Differentiable k-means clustering layer for neural network compression
Minsik Cho, Keivan A Vahid, Saurabh Adya, and Mohammad Rastegari · 2021
Cited alongside, same era.
On the transferability of pre-trained language models for low-resource programming languages
Fuxiang Chen, Fatemeh H Fard, David Lo, and Timofey Bryksin · 2022
Cited alongside, same era.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
Cited alongside, same era.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
S-lora: Serving thousands of concurrent lora adapters
Ying Sheng, Shiyi Cao, Dacheng Li, Coleman Hooper, Nicholas Lee, Shuo Yang, Christopher Chou, Banghua Zhu, Lianmin Zheng, Kurt Keutzer, et al · 2023
Later among the works it cites.
Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning
Longteng Zhang, Lin Zhang, Shaohuai Shi, Xiaowen Chu, and Bo Li · 2023
Later among the works it cites.
Relora: High-rank training through low-rank updates
Vladislav Lialin, Sherin Muckatira, Namrata Shivagunde, and Anna Rumshisky · 2023
Later among the works it cites.
Stable and low-precision training for large-scale vision-language models
Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, and Ludwig Schmidt · 2023
Later among the works it cites.
Llm-qat: Data-free quantization aware training for large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
William Fedus, Barret Zoph, and Noam Shazeer · 2022
Cited alongside, same era.
More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Tommi Kärkkäinen, Mykola Pechenizkiy, Decebal Mocanu, and Zhangyang Wang · 2022
Cited alongside, same era.
On-device training under 256kb memory
Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, and Song Han · 2022
Cited alongside, same era.
Llm. int8 (): 8-bit matrix multiplication for transformers at scale, 2022
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
Cited alongside, same era.
Paulius Micikevicius, Dusan Stosic, Neil Burgess, Marius Cornea, Pradeep Dubey, Richard Grisenthwaite, Sangwon Ha, Alexander Heinecke, Patrick Judd, John Kamalu, et al · 2022
Cited alongside, same era.
Exploring low rank training of deep neural networks
Siddhartha Rao Kamalakara, Acyr Locatelli, Bharat Venkitesh, Jimmy Ba, Yarin Gal, and Aidan N Gomez · 2022
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Cited alongside, same era.
Chatgpt: Jack of all trades, master of none
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al · 2023
Cited alongside, same era.
Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, and Vikas Chandra · 2023
Later among the works it cites.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
Later among the works it cites.
Galore: Memory-efficient llm training by gradient low-rank projection
Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, and Yuandong Tian · 2024
Closest in time.
Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al · 2024
Closest in time.
Mobilellm: Optimizing sub-billion parameter language models for on-device use cases
Zechun Liu, Changsheng Zhao, Forrest Iandola, Chen Lai, Yuandong Tian, Igor Fedorov, Yunyang Xiong, Ernie Chang, Yangyang Shi, Raghuraman Krishnamoorthi, et al · 2024
Closest in time.
Rethinking optimization and architecture for tiny language models
Yehui Tang, Fangcheng Liu, Yunsheng Ni, Yuchuan Tian, Zheyuan Bai, Yi-Qi Hu, Sichao Liu, Shangling Jui, Kai Han, and Yunhe Wang · 2024
Closest in time.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2024
Closest in time.
Chain of lora: Efficient fine-tuning of language models via residual learning
Wenhan Xia, Chengwei Qin, and Elad Hazan · 2024
Closest in time.
Lora+: Efficient low rank adaptation of large models
Soufiane Hayou, Nikhil Ghosh, and Bin Yu · 2024
Closest in time.
Flora: Low-rank adapters are secretly gradient compressors
Yongchang Hao, Yanshuai Cao, and Lili Mou · 2024
Closest in time.
Dora: Weight-decomposed low-rank adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, and Min-Hung Chen · 2024
Closest in time.
When scaling meets llm finetuning: The effect of data, model and finetuning method
Biao Zhang, Zhongtao Liu, Colin Cherry, and Orhan Firat · 2024
Closest in time.
Badam: A memory efficient full parameter training method for large language models
Qijun Luo, Hengxu Yu, and Xiao Li · 2024
Closest in time.
Lisa: Layerwise importance sampling for memory-efficient large language model fine-tuning
Rui Pan, Xiang Liu, Shizhe Diao, Renjie Pi, Jipeng Zhang, Chi Han, and Tong Zhang · 2024
Closest in time.
Llama 3 model card
AI@Meta · 2024
Closest in time.
Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
Closest in time.