Awesome Efficiency

Key papers

• Lora: Low-rank Adaptation Of Large Language Models (2021)
  Edward J. Hu, Yelong Shen, Phillip Wallis, et al.
  48.60
• Efficient Memory Management For Large Language Model Serving With Pagedattention (2023)
  Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, et al.
  46.56
• Efficient Streaming Language Models With Attention Sinks (2023)
  Guangxuan Xiao, Yuandong Tian, Beidi Chen, et al.
  37.76
• Llama: Open And Efficient Foundation Language Models (2023)
  Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.
  36.83
• Qlora: Efficient Finetuning Of Quantized Llms (2023)
  Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.
  36.23
• Minicpm: Unveiling The Potential Of Small Language Models With Scalable Training Strategies (2024)
  Shengding Hu, Yuge Tu, Xu Han, et al.
  36.01
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022)
  BigScience Workshop: Teven Le Scao, Angela Fan, Christopher Akiki, et al.
  33.79
• Layerskip: Enabling Early Exit Inference And Self-speculative Decoding (2024)
  Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, et al.
  32.46
• Prefix-tuning: Optimizing Continuous Prompts For Generation (2021)
  Xiang Lisa Li, Percy Liang
  32.38
• Omniquant: Omnidirectionally Calibrated Quantization For Large Language Models (2023)
  Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, et al.
  31.92
• Judgelm: Fine-tuned Large Language Models Are Scalable Judges (2023)
  Lianghui Zhu, Xinggang Wang, Xinlong Wang
  31.75
• Training Compute-optimal Large Language Models (2022)
  Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.
  31.52
• Deepseek LLM: Scaling Open-source Language Models With Longtermism (2024)
  Deepseek-Ai, :, Xiao Bi, et al.
  30.12
• Medusa: Simple LLM Inference Acceleration Framework With Multiple Decoding Heads (2024)
  Tianle Cai, Yuhong Li, Zhengyang Geng, et al.
  30.03
• LLM In A Flash: Efficient Large Language Model Inference With Limited Memory (2023)
  Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko, et al.
  29.72
• Agentverse: Facilitating Multi-agent Collaboration And Exploring Emergent Behaviors (2023)
  Weize Chen, Yusheng Su, Jingwei Zuo, et al.
  29.70
• Deja Vu: Contextual Sparsity For Efficient Llms At Inference Time (2023)
  Zichang Liu, Jue Wang, Tri Dao, et al.
  29.70
• Agentscope: A Flexible Yet Robust Multi-agent Platform (2024)
  Dawei Gao, Zitao Li, Xuchen Pan, et al.
  29.54
• Emergent Abilities Of Large Language Models (2022)
  Jason Wei, Yi Tay, Rishi Bommasani, et al.
  29.54
• Qa-lora: Quantization-aware Low-rank Adaptation Of Large Language Models (2023)
  Yuhui Xu, Lingxi Xie, Xiaotao Gu, et al.
  29.52
• Scaling Synthetic Data Creation With 1,000,000,000 Personas (2024)
  Tao Ge, Xin Chan, Xiaoyang Wang, et al.
  29.19
• Autogen: Enabling Next-gen LLM Applications Via Multi-agent Conversation (2023)
  Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.
  29.16
• Scaling Laws With Vocabulary: Larger Models Deserve Larger Vocabularies (2024)
  Chaofan Tao, Qian Liu, Longxu Dou, et al.
  28.35
• Mobilellm: Optimizing Sub-billion Parameter Language Models For On-device Use Cases (2024)
  Zechun Liu, Changsheng Zhao, Forrest Iandola, et al.
  28.23
• Lm-infinite: Zero-shot Extreme Length Generalization For Large Language Models (2023)
  Chi Han, Qifan Wang, Hao Peng, et al.
  27.86
• Buffer Of Thoughts: Thought-augmented Reasoning With Large Language Models (2024)
  Ling Yang, Zhaochen Yu, Tianjun Zhang, et al.
  27.84
• Shortgpt: Layers In Large Language Models Are More Redundant Than You Expect (2024)
  Xin Men, Mingyu Xu, Qingyu Zhang, et al.
  27.72
• Duoattention: Efficient Long-context LLM Inference With Retrieval And Streaming Heads (2024)
  Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, et al.
  27.44
• EASYTOOL: Enhancing Llm-based Agents With Concise Tool Instruction (2024)
  Siyu Yuan, Kaitao Song, Jiangjie Chen, et al.
  27.38
• Efficient Large Language Models: A Survey (2023)
  Zhongwei Wan, Xin Wang, Che Liu, et al.
  27.30
• Leave No Context Behind: Efficient Infinite Context Transformers With Infini-attention (2024)
  Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal
  27.29
• Flashrag: A Modular Toolkit For Efficient Retrieval-augmented Generation Research (2024)
  Jiajie Jin, Yutao Zhu, Guanting Dong, et al.
  26.95
• Culturax: A Cleaned, Enormous, And Multilingual Dataset For Large Language Models In 167 Languages (2023)
  Thuat Nguyen, Chien van Nguyen, Viet Dac Lai, et al.
  26.68
• SOLAR 10.7B: Scaling Large Language Models With Simple Yet Effective Depth Up-scaling (2023)
  Dahyun Kim, Chanjun Park, Sanghoon Kim, et al.
  26.34
• Triforce: Lossless Acceleration Of Long Sequence Generation With Hierarchical Speculative Decoding (2024)
  Hanshi Sun, Zhuoming Chen, Xinyu Yang, et al.
  26.11
• Minference 1.0: Accelerating Pre-filling For Long-context Llms Via Dynamic Sparse Attention (2024)
  Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, et al.
  25.85
• SVD-LLM: Truncation-aware Singular Value Decomposition For Large Language Model Compression (2024)
  Xin Wang, Yu Zheng, Zhongwei Wan, et al.
  25.83
• Memgpt: Towards Llms As Operating Systems (2023)
  Charles Packer, Sarah Wooders, Kevin Lin, et al.
  25.76
• Babilong: Testing The Limits Of Llms With Long Context Reasoning-in-a-haystack (2024)
  Yuri Kuratov, Aydar Bulatov, Petr Anokhin, et al.
  25.74
• Scaling Retrieval-based Language Models With A Trillion-token Datastore (2024)
  Rulin Shao, Jacqueline He, Akari Asai, et al.
  25.71
• Evoprompt: Connecting Llms With Evolutionary Algorithms Yields Powerful Prompt Optimizers (2023)
  Qingyan Guo, Rui Wang, Junliang Guo, et al.
  25.70
• Infllm: Training-free Long-context Extrapolation For Llms With An Efficient Context Memory (2024)
  Chaojun Xiao, Pengle Zhang, Xu Han, et al.
  25.56
• Cacheblend: Fast Large Language Model Serving For RAG With Cached Knowledge Fusion (2024)
  Jiayi Yao, Hanchen Li, Yuhan Liu, et al.
  25.46
• When Scaling Meets LLM Finetuning: The Effect Of Data, Model And Finetuning Method (2024)
  Biao Zhang, Zhongtao Liu, Colin Cherry, et al.
  25.34
• Controlllm: Augment Language Models With Tools By Searching On Graphs (2023)
  Zhaoyang Liu, Zeqiang Lai, Zhangwei Gao, et al.
  24.66
• MA-LMM: Memory-augmented Large Multimodal Model For Long-term Video Understanding (2024)
  Bo He, Hengduo Li, Young Kyun Jang, et al.
  24.50
• Shortened Llama: Depth Pruning For Large Language Models With Comparison Of Retraining Methods (2024)
  Bo-Kyeong Kim, Geonmin Kim, Tae-Ho Kim, et al.
  24.29
• Federatedscope-llm: A Comprehensive Package For Fine-tuning Large Language Models In Federated Learning (2023)
  Weirui Kuang, Bingchen Qian, Zitao Li, et al.
  24.26
• Compact Language Models Via Pruning And Knowledge Distillation (2024)
  Saurav Muralidharan, Sharath Turuvekere Sreenivas, Raviraj Joshi, et al.
  24.15
• Platypus: Quick, Cheap, And Powerful Refinement Of Llms (2023)
  Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz
  24.03
• Atom: Low-bit Quantization For Efficient And Accurate LLM Serving (2023)
  Yilong Zhao, Chien-Yu Lin, Kan Zhu, et al.
  23.81
• Black-box Prompt Optimization: Aligning Large Language Models Without Model Training (2023)
  Jiale Cheng, Xiao Liu, Kehan Zheng, et al.
  23.78
• Routerbench: A Benchmark For Multi-llm Routing System (2024)
  Qitian Jason Hu, Jacob Bieker, Xiuyu Li, et al.
  23.73
• Tinybenchmarks: Evaluating Llms With Fewer Examples (2024)
  Felipe Maia Polo, Lucas Weber, Leshem Choshen, et al.
  23.68
• Simple And Scalable Strategies To Continually Pre-train Large Language Models (2024)
  Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, et al.
  23.62
• Discovering The Gems In Early Layers: Accelerating Long-context Llms With 1000x Input Token Reduction (2024)
  Zhenmei Shi, Yifei Ming, Xuan-Phi Nguyen, et al.
  23.61
• Optimize Weight Rounding Via Signed Gradient Descent For The Quantization Of Llms (2023)
  Wenhua Cheng, Weiwei Zhang, Haihao Shen, et al.
  23.56
• Cobra: Extending Mamba To Multi-modal Large Language Model For Efficient Inference (2024)
  Han Zhao, Min Zhang, Wei Zhao, et al.
  23.43
• Think: Thinner Key Cache By Query-driven Pruning (2024)
  Yuhui Xu, Zhanming Jie, Hanze Dong, et al.
  23.34
• GEAR: An Efficient KV Cache Compression Recipe For Near-lossless Generative Inference Of LLM (2024)
  Hao Kang, Qingru Zhang, Souvik Kundu, et al.
  23.22