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The advent of large language models (LLMs) revolutionized natural language processing applications, and running LLMs on edge devices has become increasingly attractive for reasons including reduced latency, data localization, and personalized user experiences.
Adaptive mixtures of local experts
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Mixture of experts: a literature survey
Saeed Masoudnia and Reza Ebrahimpour · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Improving language understanding by generative pre-training
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Computational operations research exchange (core): A cyber-infrastructure for analytics
Yunxiao Deng, Carl Kesselman, Suvrajeet Sen, and Jiajun Xu · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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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
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Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
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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
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Gpt-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti · 2020
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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 2020
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Near-memory processing in action: Accelerating personalized recommendation with axdimm
Liu Ke, Xuan Zhang, Jinin So, Jong-Geon Lee, Shin-Haeng Kang, Sukhan Lee, Songyi Han, YeonGon Cho, Jin Hyun Kim, Yongsuk Kwon, et al · 2021
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Aquabolt-xl: Samsung hbm2-pim with in-memory processing for ml accelerators and beyond
Jin Hyun Kim, Shin-haeng Kang, Sukhan Lee, Hyeonsu Kim, Woongjae Song, Yuhwan Ro, Seungwon Lee, David Wang, Hyunsung Shin, Bengseng Phuah, et al · 2021
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova Del Moral, Yacine Jernite, Abhishek Thakur, Patrick Von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, et al · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al · 2022
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Towards understanding the mixture-of-experts layer in deep learning
Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, and Yuanzhi Li · 2022
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Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
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Glam: Efficient scaling of language models with mixture-of-experts
Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, et al · 2022
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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On-device training under 256kb memory
Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, and Song Han · 2022
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Overcoming oscillations in quantization-aware training
Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, and Tijmen Blankevoort · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Gqa: Training generalized multi-query transformer models from multi-head checkpoints
Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai · 2023
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The falcon series of open language models
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Mérouane Debbah, Étienne Goffinet, Daniel Hesslow, Julien Launay, Quentin Malartic, et al · 2023
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Revolutionizing mobile interaction: Enabling a 3 billion parameter gpt llm on mobile
Samuel Carreira, Tomás Marques, José Ribeiro, and Carlos Grilo · 2023
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Huatuogpt-ii, one-stage training for medical adaption of llms
Junying Chen, Xidong Wang, Anningzhe Gao, Feng Jiang, Shunian Chen, Hongbo Zhang, Dingjie Song, Wenya Xie, Chuyi Kong, Jianquan Li, et al · 2023
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A performance evaluation of a quantized large language model on various smartphones
Tolga Çöplü, Marc Loedi, Arto Bendiken, Mykhailo Makohin, Joshua J Bouw, and Stephen Cobb · 2023
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Decoder-only or encoder-decoder? interpreting language model as a regularized encoder-decoder
Zihao Fu, Wai Lam, Qian Yu, Anthony Man-Cho So, Shengding Hu, Zhiyuan Liu, and Nigel Collier · 2023
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llama.cpp: Lightweight library for approximate nearest neighbors and maximum inner product search
Georgi Gerganov · 2023
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Multimodal-gpt: A vision and language model for dialogue with humans
Tao Gong, Chengqi Lyu, Shilong Zhang, Yudong Wang, Miao Zheng, Qian Zhao, Kuikun Liu, Wenwei Zhang, Ping Luo, and Kai Chen · 2023
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Minillm: Knowledge distillation of large language models
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang · 2023
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Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Challenges and applications of large language models
Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy · 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
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Deja vu: Contextual sparsity for efficient llms at inference time
Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, et al · 2023
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Making llms worth every penny: Resource-limited text classification in banking
Lefteris Loukas, Ilias Stogiannidis, Odysseas Diamantopoulos, Prodromos Malakasiotis, and Stavros Vassos · 2023
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Amama Mahmood, Junxiang Wang, Bingsheng Yao, Dakuo Wang, and Chien-Ming Huang · 2023
Cited alongside, same era.
Skeleton-of-thought: Large language models can do parallel decoding
Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, and Yu Wang · 2023
Cited alongside, same era.
A study of generative large language model for medical research and healthcare
Cheng Peng, Xi Yang, Aokun Chen, Kaleb E Smith, Nima PourNejatian, Anthony B Costa, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, et al · 2023
Cited alongside, same era.
Matrix compression via randomized low rank and low precision factorization
Rajarshi Saha, Varun Srivastava, and Mert Pilanci · 2023
Cited alongside, same era.
Enhancing trust in llm-based ai automation agents: New considerations and future challenges
The era of 1-bit llms: All large language models are in 1.58 bits
Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, and Furu Wei · 2024
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Edge ai market
Market.us · 2024
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Mm1: Methods, analysis & insights from multimodal llm pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al · 2024
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Openelm: An efficient language model family with open training and inference framework
Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Seyed Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, et al · 2024
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Meta llama 3
Meta · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sivan Schwartz, Avi Yaeli, and Segev Shlomov · 2023
Cited alongside, same era.
Powerinfer: Fast large language model serving with a consumer-grade gpu
Yixin Song, Zeyu Mi, Haotong Xie, and Haibo Chen · 2023
Cited alongside, same era.
Gpt4 response time
taivo · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Cited alongside, same era.
Internlm: A multilingual language model with progressively enhanced capabilities, 2023
InternLM Team · 2023
Cited alongside, same era.
MLC-LLM, 2023
MLC team · 2023
Cited alongside, same era.
Privatelora for efficient privacy preserving llm
Yiming Wang, Yu Lin, Xiaodong Zeng, and Guannan Zhang · 2023
Cited alongside, same era.
Multimodal large language models: A survey
Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and S Yu Philip · 2023
Cited alongside, same era.
Rithesh Murthy, Liangwei Yang, Juntao Tan, Tulika Manoj Awalgaonkar, Yilun Zhou, Shelby Heinecke, Sachin Desai, Jason Wu, Ran Xu, Sarah Tan, et al · 2024
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Using an llm to help with code understanding
Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, and Brad Myers · 2024
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Towards modular llms by building and reusing a library of loras
Oleksiy Ostapenko, Zhan Su, Edoardo Maria Ponti, Laurent Charlin, Nicolas Le Roux, Matheus Pereira, Lucas Caccia, and Alessandro Sordoni · 2024
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Any-precision llm: Low-cost deployment of multiple, different-sized llms
Yeonhong Park, Jake Hyun, SangLyul Cho, Bonggeun Sim, and Jae W Lee · 2024
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Scalable language model with generalized continual learning
Bohao Peng, Zhuotao Tian, Shu Liu, Mingchang Yang, and Jiaya Jia · 2024
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Plaud note summarizer
Plaud · 2024
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executorch: Overview
PyTorch · 2024
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Interactive continual learning: Fast and slow thinking
Biqing Qi, Xinquan Chen, Junqi Gao, Dong Li, Jianxing Liu, Ligang Wu, and Bowen Zhou · 2024
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Qwen 2-0.5b
Ali Cloud Qwen Team · 2024
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Timechat: A time-sensitive multimodal large language model for long video understanding
Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, and Lu Hou · 2024
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Jetmoe: Reaching llama2 performance with 0.1 m dollars
Yikang Shen, Zhen Guo, Tianle Cai, and Zengyi Qin · 2024
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Continual learning of large language models: A comprehensive survey
Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, and Hao Wang · 2024
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Using an llm to turn sign spottings into spoken language sentences
Ozge Mercanoglu Sincan, Necati Cihan Camgoz, and Richard Bowden · 2024
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Dolma: An open corpus of three trillion tokens for language model pretraining research
Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Chandu, Jennifer Dumas, Yanai Elazar, et al · 2024
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Towards greener llms: Bringing energy-efficiency to the forefront of llm inference
Jovan Stojkovic, Esha Choukse, Chaojie Zhang, Inigo Goiri, and Josep Torrellas · 2024
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Large language models for forecasting and anomaly detection: A systematic literature review
Jing Su, Chufeng Jiang, Xin Jin, Yuxin Qiao, Tingsong Xiao, Hongda Ma, Rong Wei, Zhi Jing, Jiajun Xu, and Junhong Lin · 2024
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Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
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Vllm documentation
VLLM Project Team · 2024
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Drivevlm: The convergence of autonomous driving and large vision-language models
Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Chenxu Hu, Yang Wang, Kun Zhan, Peng Jia, Xianpeng Lang, and Hang Zhao · 2024
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Minicpm-llama3-v 2.5
Modelbest Inc. Tsinghua University · 2024
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Towards generalist biomedical ai
Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Charles Lau, Ryutaro Tanno, Ira Ktena, et al · 2024
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Werner Vogels · 2024
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The evolution of multimodal model architectures
Shakti N Wadekar, Abhishek Chaurasia, Aman Chadha, and Eugenio Culurciello · 2024
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A multimodal approach to device-directed speech detection with large language models
Dominik Wagner, Alexander Churchill, Siddharth Sigtia, Panayiotis Georgiou, Matt Mirsamadi, Aarshee Mishra, and Erik Marchi · 2024
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Software/hardware co-design for llm and its application for design verification
Lily Jiaxin Wan, Yingbing Huang, Yuhong Li, Hanchen Ye, Jinghua Wang, Xiaofan Zhang, and Deming Chen · 2024
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Grant Wilkins, Srinivasan Keshav, and Richard Mortier · 2024
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Continual learning for large language models: A survey
Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, and Gholamreza Haffari · 2024
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Large multimodal agents: A survey
Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan, and Guanbin Li · 2024
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Ensemble variance reduction methods for stochastic mixed-integer programming and their application to the stochastic facility location problem
Jiajun Xu and Suvrajeet Sen · 2024
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Llm as a system service on mobile devices
Wangsong Yin, Mengwei Xu, Yuanchun Li, and Xuanzhe Liu · 2024
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Yi: Open foundation models by 01. ai
Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al · 2024
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Wip: An on-device llm-based approach to query privacy protection
Yizhen Yuan, Rui Kong, Yuanchun Li, and Yunxin Liu · 2024
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Zoom meeting summarizer
Zoom · 2024
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