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Large language models (LLMs) are omnipresent, however their practical deployment is challenging due to their ever increasing computational and memory demands.
Fixed-point representation & fractional math
Erick L Oberstar · 2007
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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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
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Post-training 4-bit quantization of convolution networks for rapid-deployment
Ron Banner, Yury Nahshan, Elad Hoffer, and Daniel Soudry · 2018
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
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Low-bit quantization of neural networks for efficient inference
Yoni Choukroun, Eli Kravchik, Fan Yang, and Pavel Kisilev · 2019
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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Same, same but different: Recovering neural network quantization error through weight factorization
Eldad Meller, Alexander Finkelstein, Uri Almog, and Mark Grobman · 2019
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Data-free quantization through weight equalization and bias correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Improving neural network quantization without retraining using outlier channel splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, and Zhiru Zhang · 2019
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Lsq+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat, Jinwon Lee, Markus Nagel, Tijmen Blankevoort, and Nojun Kwak · 2020
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
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Zeroq: A novel zero shot quantization framework
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
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Learned step size quantization
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S. Modha · 2020
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Characterising bias in compressed models
Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, and Emily Denton · 2020
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Improving post training neural quantization: Layer-wise calibration and integer programming
Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, and Daniel Soudry · 2020
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Up or down? Adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort · 2020
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Loftq: Lora-fine-tuning-aware quantization for large language models
Yixiao Li, Yifan Yu, Chen Liang, Pengcheng He, Nikos Karampatziakis, Weizhu Chen, and Tuo Zhao · 2023
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Awq: Activation-aware weight quantization for llm compression and acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han · 2023
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Long-range zero-shot generative deep network quantization
Yan Luo, Yangcheng Gao, Zhao Zhang, Jicong Fan, Haijun Zhang, and Mingliang Xu · 2023
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Omniquant: Omnidirectionally calibrated quantization for large language models
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo · 2023
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SlimPajama: A 627B token cleaned and deduplicated version of RedPajama
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2020
Cited alongside, same era.
Understanding and overcoming the challenges of efficient transformer quantization
Yelysei Bondarenko, Markus Nagel, and Tijmen Blankevoort · 2021
Cited alongside, same era.
A framework for few-shot language model evaluation
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, et al · 2021
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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
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Bert busters: Outlier dimensions that disrupt transformers
Olga Kovaleva, Saurabh Kulshreshtha, Anna Rogers, and Anna Rumshisky · 2021
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Brecq: Pushing the limit of post-training quantization by block reconstruction
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu · 2021
Cited alongside, same era.
A white paper on neural network quantization
Markus Nagel, Marios Fournarakis, Rana Ali Amjad, Yelysei Bondarenko, Mart van Baalen, and Blankevoort Tijmen · 2021
Cited alongside, same era.
Daria Soboleva, Faisal Al-Khateeb, Robert Myers, Jacob R Steeves, Joel Hestness, and Nolan Dey · 2023
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Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, and Xianglong Liu · 2023
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Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han · 2023
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Qa-lora: Quantization-aware low-rank adaptation of large language models
Yuhui Xu, Lingxi Xie, Xiaotao Gu, Xin Chen, Heng Chang, Hengheng Zhang, Zhensu Chen, Xiaopeng Zhang, and Qi Tian · 2023
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Rptq: Reorder-based post-training quantization for large language models
Zhihang Yuan, Lin Niu, Jiawei Liu, Wenyu Liu, Xinggang Wang, Yuzhang Shang, Guangyu Sun, Qiang Wu, Jiaxiang Wu, and Bingzhe Wu · 2023
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Llama 3 model card, 2024
AI@Meta · 2024
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Quantizable transformers: Removing outliers by helping attention heads do nothing
Yelysei Bondarenko, Markus Nagel, and Tijmen Blankevoort · 2024
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Quip: 2-bit quantization of large language models with guarantees
Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, and Christopher M De Sa · 2024
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Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2024
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L4q: Parameter efficient quantization-aware training on large language models via lora-wise lsq
Hyesung Jeon, Yulhwa Kim, and Jae-joon Kim · 2024
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Memory-efficient fine-tuning of compressed large language models via sub-4-bit integer quantization
Jeonghoon Kim, Jung Hyun Lee, Sungdong Kim, Joonsuk Park, Kang Min Yoo, Se Jung Kwon, and Dongsoo Lee · 2024
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Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models
Changhun Lee, Jungyu Jin, Taesu Kim, Hyungjun Kim, and Eunhyeok Park · 2024
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Qserve: W4a8kv4 quantization and system co-design for efficient llm serving
Yujun Lin, Haotian Tang, Shang Yang, Zhekai Zhang, Guangxuan Xiao, Chuang Gan, and Song Han · 2024
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Xuan Shen, Zhenglun Kong, Changdi Yang, Zhaoyang Han, Lei Lu, Peiyan Dong, Cheng Lyu, Chih-hsiang Li, Xuehang Guo, Zhihao Shu, et al · 2024
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Massive activations in large language models
Mingjie Sun, Xinlei Chen, J Zico Kolter, and Zhuang Liu · 2024
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Easyquant: An efficient data-free quantization algorithm for llms
Hanlin Tang, Yifu Sun, Decheng Wu, Kai Liu, Jianchen Zhu, and Zhanhui Kang · 2024
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Lqer: Low-rank quantization error reconstruction for llms
Cheng Zhang, Jianyi Cheng, George A Constantinides, and Yiren Zhao · 2024
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