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To combat the memory bandwidth-bound nature of autoregressive LLM inference, previous research has proposed the speculative decoding frame-work.
SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
Fast transformer decoding: One write-head is all you need, 2019
Noam Shazeer · 2019
Earlier work this paper cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2020
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2020
Earlier work this paper cites.
Llm.int8(): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
Earlier work this paper cites.
Orca: A distributed serving system for Transformer-Based generative models
Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, Soojeong Kim, and Byung-Gon Chun · 2022
Earlier work this paper cites.
GQA: Training generalized multi-query transformer models from multi-head checkpoints
Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebron, and Sumit Sanghai · 2023
Earlier work this paper cites.
Accelerating large language model decoding with speculative sampling, 2023
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper · 2023
Earlier work this paper cites.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
Earlier work this paper cites.
Sparsegpt: massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh · 2023
Earlier work this paper cites.
OPTQ: Accurate quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2023
Cited alongside, same era.
Breaking the sequential dependency of llm inference using lookahead decoding, November 2023
Yichao Fu, Peter Bailis, Ion Stoica, and Hao Zhang · 2023
Cited alongside, same era.
Assisted generation: a new direction toward low-latency text generation, May 2023
Joao Gante · 2023
Cited alongside, same era.
Rest: Retrieval-based speculative decoding, 2023
Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D Lee, and Di He · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention, 2023
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica · 2023
Cited alongside, same era.
Fast inference from transformers via speculative decoding
Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
Later among the works it cites.
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
Later among the works it cites.
Judging LLM-as-a-judge with MT-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
Later among the works it cites.
Llm in a flash: Efficient large language model inference with limited memory, 2024
Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko, Karen Khatamifard, Minsik Cho, Carlo C Del Mundo, Mohammad Rastegari, and Mehrdad Farajtabar · 2024
Closest in time.
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
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Yaniv Leviathan, Matan Kalman, and Yossi Matias · 2023
Cited alongside, same era.
Specinfer: Accelerating generative large language model serving with speculative inference and token tree verification, 2023
Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng, Zeyu Wang, Rae Ying Yee Wong, Alan Zhu, Lijie Yang, Xiaoxiang Shi, Chunan Shi, Zhuoming Chen, Daiyaan Arfeen, Reyna Abhyankar, and Zhihao Jia · 2023
Cited alongside, same era.
Sharegpt, 2023
ShareGPT · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Accelerating LLM inference with staged speculative decoding
Benjamin Frederick Spector and Christopher Re · 2023
Cited alongside, same era.
Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
Cited alongside, same era.
Hugging face trainer
HuggingFace
Cited in the paper.
Closest in time.
NEFTune: Noisy embeddings improve instruction finetuning
Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2024
Closest in time.
Eagle: Speculative sampling requires rethinking feature uncertainty, 2024
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang · 2024
Closest in time.
Accelerating production llms with combined token/embedding speculators
Davis Wertheimer, Joshua Rosenkranz, Thomas Parnell, Sahil Suneja, Pavithra Ranganathan, Raghu Ganti, and Mudhakar Srivatsa · 2024
Closest in time.
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, and Zhifang Sui · 2024
Closest in time.
Recurrent drafter for fast speculative decoding in large language models
Aonan Zhang, Chong Wang, Yi Wang, Xuanyu Zhang, and Yunfei Cheng · 2024
Closest in time.
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 · 2024
Closest in time.