DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs

Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Original

Wu, H.; Judd, P.; Zhang, X.; Isaev, M.; and Micikevicius, P. 2020 · 2004

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Pointer Sentinel Mixture Models

Merity, S.; Xiong, C.; Bradbury, J.; and Socher, R. 2007 · 2007

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Attention is All you Need

Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L. u.; and Polosukhin, I. 2017 · 2017

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SignSGD: Compressed Optimisation for Non-Convex Problems

Bernstein, J.; Wang, Y.-X.; Azizzadenesheli, K.; and Anandkumar, A. 2018 · 2018

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Post Training 4-bit Quantization of Convolutional Networks for Rapid-Deployment

Banner, R.; Nahshan, Y.; and Soudry, D. 2019 · 2019

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The Pile: An 800GB Dataset of Diverse Text for Language Modeling

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Gao, L.; Biderman, S.; Black, S.; Golding, L.; Hoppe, T.; Foster, C.; Phang, J.; He, H.; Thite, A.; Nabeshima, N.; et al. 2020 · 2020

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Accurate Post Training Quantization with Small Calibration Sets

Hubara, I.; Nahshan, Y.; Hanani, Y.; Banner, R.; and Soudry, D. 2021 · 2021

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Loss Aware Post-Training Quantization

Nahshan, Y.; Chmiel, B.; Baskin, C.; Zheltonozhskii, E.; Banner, R.; Bronstein, A. M.; and Mendelson, A. 2021 · 2021

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FP8 Quantization: The Power of the Exponent

Kuzmin, A.; van Baalen, M.; Ren, Y.; Nagel, M.; Peters, J.; and Blankevoort, T. 2022 · 2022

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Outlier Suppression: Pushing the Limit of Low-Bit Transformer Language Models

Wei, X.; Zhang, Y.; Zhang, X.; Gong, R.; Zhang, S.; Zhang, Q.; Yu, F.; and Liu, X. 2022 · 2022

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OPT: Open Pre-trained Transformer Language Models

Original

Zhang, S.; Roller, S.; Goyal, N.; Artetxe, M.; Chen, M.; Chen, S.; Dewan, C.; Diab, M.; Li, X.; Lin, X. V.; et al. 2022 · 2022

Cited alongside, same era.

QLoRA: Efficient Finetuning of Quantized LLMs

Dettmers, T.; Pagnoni, A.; Holtzman, A.; and Zettlemoyer, L. 2023 · 2023

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The Case for 4-Bit Precision: K-Bit Inference Scaling Laws

Dettmers, T.; and Zettlemoyer, L. 2023 · 2023

Cited alongside, same era.

SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot

Frantar, E.; and Alistarh, D. 2023 · 2023

Cited alongside, same era.

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Xiao, G.; Lin, J.; Seznec, M.; Wu, H.; Demouth, J.; and Han, S. 2023 · 2023

Later among the works it cites.

NF4 Isn’t Information Theoretically Optimal (and That’s Good)

Original

Yoshida, D. 2023 · 2023

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QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning

Original

Chen, J.-M.; Chao, Y.-H.; Wang, Y.-J.; Shieh, M.-D.; Hsu, C.-C.; and Lin, W.-F. 2024 · 2024

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SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Dettmers, T.; Svirschevski, R.; Egiazarian, V.; Kuznedelev, D.; Frantar, E.; Ashkboos, S.; Borzunov, A.; Hoefler, T.; and Alistarh, D. 2024 · 2024

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SqueezeLLM: Dense-and-Sparse Quantization

Original