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Large Language Models (LLMs) inherently use autoregressive decoding, which lacks parallelism in inference and results in significantly slow inference speed.
Rouge: A package for automatic evaluation of summaries
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Attention is all you need
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Blockwise parallel decoding for deep autoregressive models
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Training verifiers to solve math word problems
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021 · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. 2021 · 2021
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. 2022 · 2022
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Chatgpt: Chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
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Fast and robust early-exiting framework for autoregressive language models with synchronized parallel decoding
Sangmin Bae, Jongwoo Ko, Hwanjun Song, and Se-Young Yun. 2023 · 2023
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Exponentially faster language modelling
Peter Belcak and Roger Wattenhofer. 2023 · 2023
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Accelerating large language model decoding with speculative sampling
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. 2023 · 2023
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Model tells you what to discard: Adaptive kv cache compression for llms
Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao. 2023 · 2023
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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 · 2023
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Giovanni Monea, Armand Joulin, and Edouard Grave. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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A comprehensive study on post-training quantization for large language models
Speculative decoding with big little decoder
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Eagle: Speculative sampling requires rethinking feature uncertainty
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The era of 1-bit llms: All large language models are in 1.58 bits
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Specinfer: Accelerating large language model serving with tree-based speculative inference and verification
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Zhewei Yao, Cheng Li, Xiaoxia Wu, Stephen Youn, and Yuxiong He. 2023 · 2023
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Sequoia: Scalable, robust, and hardware-aware speculative decoding
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Qlora: Efficient finetuning of quantized llms
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Glide with a cape: A low-hassle method to accelerate speculative decoding
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Distillspec: Improving speculative decoding via knowledge distillation
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