Fetching the paper…
Reading the bibliography…
As the usage of large language models (LLMs) grows, performing efficient inference with these models becomes increasingly important.
Gumbel-max trick and weighted reservoir sampling, 2014
Tim Vieira · 2014
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
Earlier work this paper cites.
Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
Earlier work this paper cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
Earlier work this paper cites.
Blockwise parallel decoding for deep autoregressive models
Mitchell Stern, Noam Shazeer, and Jakob Uszkoreit · 2018
Earlier work this paper cites.
Openwebtext corpus, 2019
Aaron Gokaslan and Vanya Cohen · 2019
Earlier work this paper cites.
Data-free quantization through weight equalization and bias correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling · 2019
Earlier work this paper cites.
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, et al · 2019
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 · 2019
Earlier work this paper cites.
Distilling task-specific knowledge from bert into simple neural networks
Raphael Tang, Yao Lu, Linqing Liu, Lili Mou, Olga Vechtomova, and Jimmy Lin · 2019
Earlier work this paper cites.
Huggingface’s 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 · 2019
Earlier work this paper cites.
Improving neural network quantization without retraining using outlier channel splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, and Zhiru Zhang · 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.
Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
Cited alongside, same era.
Cuda, release: 10.2.89, 2020
NVIDIA, Péter Vingelmann, and Frank H.P. Fitzek · 2020
Cited alongside, same era.
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Cited alongside, same era.
Deepspeed inference: Enabling efficient inference of transformer models at unprecedented scale
Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, et al · 2022
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Later among the works it cites.
Big little transformer decoder
Sehoon Kim, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Amir Gholami, and Kurt Keutzer · 2023
Later among the works it cites.
Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica · 2023
Later among the works it cites.
Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias · 2023
Later among the works it cites.
AWQ: activation-aware weight quantization for LLM compression and acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
PaLM: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
Cited alongside, same era.
Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
Cited alongside, same era.
Llm.int8(): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
Cited alongside, same era.
GPTQ: accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2022
Cited alongside, same era.
Orca: A distributed serving system for transformer-based generative models
Gyeong-In Yu and Joo Seong Jeong · 2022
Cited alongside, same era.
Colt5: Faster long-range transformers with conditional computation
Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontañón, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, et al · 2023
Cited alongside, same era.
Later among the works it cites.
Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng, Zeyu Wang, Rae Ying Yee Wong, Zhuoming Chen, Daiyaan Arfeen, Reyna Abhyankar, and Zhihao Jia · 2023
Later among the works it cites.
Flexgen: High-throughput generative inference of large language models with a single GPU
Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, and Ce Zhang · 2023
Later among the works it cites.
Sheared llama: Accelerating language model pre-training via structured pruning
Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, and Danqi Chen · 2023
Later among the works it cites.
Llmcad: Fast and scalable on-device large language model inference
Daliang Xu, Wangsong Yin, Xin Jin, Ying Zhang, Shiyun Wei, Mengwei Xu, and Xuanzhe Liu · 2023
Later among the works it cites.
Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning llms to high sparsity
Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, and Shiwei Liu · 2023
Later among the works it cites.
Draft & verify: Lossless large language model acceleration via self-speculative decoding
Jun Zhang, Jue Wang, Huan Li, Lidan Shou, Ke Chen, Gang Chen, and Sharad Mehrotra · 2023
Later among the works it cites.
Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
Later among the works it cites.
Distillspec: Improving speculative decoding via knowledge distillation
Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, and Rishabh Agarwal · 2023
Later among the works it cites.
Medusa: Simple llm inference acceleration framework with multiple decoding heads, 2024
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, and Tri Dao · 2024
Closest in time.
Glide with a cape: A low-hassle method to accelerate speculative decoding
Cunxiao Du, Jing Jiang, Yuanchen Xu, Jiawei Wu, Sicheng Yu, Yongqi Li, Shenggui Li, Kai Xu, Liqiang Nie, Zhaopeng Tu, and Yang You · 2024
Closest in time.
Eagle: Speculative sampling requires rethinking feature uncertainty
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang · 2024
Closest in time.