Fetching the paper…
Reading the bibliography…
Quantizing model weights is critical for reducing the communication and inference costs of large models.
Pyramid methods in image processing
E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden · 1984
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
Earlier work this paper cites.
Learning ordered representations with nested dropout
O. Rippel, M. Gelbart, and R. Adams · 2014
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
E. L. Denton, S. Chintala, R. Fergus, et al · 2015
Earlier work this paper cites.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. M. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the AI2 reasoning challenge
P. Clark, I. Cowhey, O. Etzioni, T. Khot, A. Sabharwal, C. Schoenick, and O. Tafjord · 2018
Earlier work this paper cites.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko · 2018
Earlier work this paper cites.
J. Yu, L. Yang, N. Xu, J. Yang, and T. Huang · 2018
Earlier work this paper cites.
Boolq: Exploring the surprising difficulty of natural yes/no questions
C. Clark, K. Lee, M. Chang, T. Kwiatkowski, M. Collins, and K. Toutanova · 2019
Earlier work this paper cites.
Any-precision deep neural networks
H. Yu, H. Li, H. Shi, T. S. Huang, and G. Hua · 2019
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi · 2019
Earlier work this paper cites.
PIQA: reasoning about physical commonsense in natural language
Y. Bisk, R. Zellers, R. L. Bras, J. Gao, and Y. Choi · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020
Cited alongside, same era.
Winogrande: An adversarial winograd schema challenge at scale
K. Sakaguchi, R. L. Bras, C. Bhagavatula, and Y. Choi · 2020
Cited alongside, same era.
Pareto-optimal quantized resnet is mostly 4-bit
A. Abdolrashidi, L. Wang, S. Agrawal, J. Malmaud, O. Rybakov, C. Leichner, and L. Lew · 2021
Cited alongside, same era.
Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale
T. Dettmers, M. Lewis, Y. Belkada, and L. Zettlemoyer · 2022
Cited alongside, same era.
Gptq: Accurate post-training quantization for generative pre-trained transformers
E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh · 2022
Cited alongside, same era.
Framequant: Flexible low-bit quantization for transformers
H. Adepu, Z. Zeng, L. Zhang, and V. Singh · 2024
Later among the works it cites.
Quarot: Outlier-free 4-bit inference in rotated llms
S. Ashkboos, A. Mohtashami, M. L. Croci, B. Li, M. Jaggi, D. Alistarh, T. Hoefler, and J. Hensman · 2024
Later among the works it cites.
Quip: 2-bit quantization of large language models with guarantees
J. Chee, Y. Cai, V. Kuleshov, and C. M. De Sa · 2024
Later among the works it cites.
Efficientqat: Efficient quantization-aware training for large language models
M. Chen, W. Shao, P. Xu, J. Wang, P. Gao, K. Zhang, Y. Qiao, and P. Luo · 2024
Later among the works it cites.
Bitdistiller: Unleashing the potential of sub-4-bit llms via self-distillation
D. Du, Y. Zhang, S. Cao, J. Guo, T. Cao, X. Chu, and N. Xu · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Kusupati, G. Bhatt, A. Rege, M. Wallingford, A. Sinha, V. Ramanujan, W. Howard-Snyder, K. Chen, S. Kakade, P. Jain, et al · 2022
Cited alongside, same era.
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
Cited alongside, same era.
Spqr: A sparse-quantized representation for near-lossless llm weight compression
T. Dettmers, R. Svirschevski, V. Egiazarian, D. Kuznedelev, E. Frantar, S. Ashkboos, A. Borzunov, T. Hoefler, and D. Alistarh · 2023
Cited alongside, same era.
Matformer: Nested transformer for elastic inference
F. Devvrit, S. Kudugunta, A. Kusupati, T. Dettmers, K. Chen, I. Dhillon, Y. Tsvetkov, H. Hajishirzi, S. Kakade, A. Farhadi, P. Jain, et al · 2023
Cited alongside, same era.
A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. de Las Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, L. R. Lavaud, M. Lachaux, P. Stock, T. L. Scao, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed · 2023
Cited alongside, same era.
Awq: Activation-aware weight quantization for llm compression and acceleration
J. Lin, J. Tang, H. Tang, S. Yang, X. Dang, and S. Han · 2023
Cited alongside, same era.
Omniquant: Omnidirectionally calibrated quantization for large language models
W. Shao, M. Chen, Z. Zhang, P. Xu, L. Zhao, Z. Li, K. Zhang, P. Gao, Y. Qiao, and P. Luo · 2023
Cited alongside, same era.
Later among the works it cites.
A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Yang, A. Fan, et al · 2024
Later among the works it cites.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
G. G Team, P. Georgiev, V. I. Lei, R. Burnell, L. Bai, A. Gulati, G. Tanzer, D. Vincent, Z. Pan, S. Wang, et al · 2024
Later among the works it cites.
Gemma 2: Improving open language models at a practical size
Gemma-Team · 2024
Later among the works it cites.
Squeezellm: Dense-and-sparse quantization
S. Kim, C. Hooper, A. Gholami, Z. Dong, X. Li, S. Shen, M. W. Mahoney, and K. Keutzer · 2024
Later among the works it cites.
LLM-QAT: data-free quantization aware training for large language models
Z. Liu, B. Oguz, C. Zhao, E. Chang, P. Stock, Y. Mehdad, Y. Shi, R. Krishnamoorthi, and V. Chandra · 2024
Later among the works it cites.
Affinequant: Affine transformation quantization for large language models
Y. Ma, H. Li, X. Zheng, F. Ling, X. Xiao, R. Wang, S. Wen, F. Chao, and R. Ji · 2024
Later among the works it cites.
P. A. Nair and A. S. Suggala · 2024
Later among the works it cites.
A simple and effective pruning approach for large language models
M. Sun, Z. Liu, A. Bair, and J. Z. Kolter · 2024
Later among the works it cites.