pruning.md

Network Pruning

Title & Authors	Introduction	Links
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot Elias Frantar, Dan Alistarh		Github paper
LLM-Pruner: On the Structural Pruning of Large Language Models Xinyin Ma, Gongfan Fang, Xinchao Wang		Github paper
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Zhangyang Wang		Github Paper
Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity Haojun Xia, Zhen Zheng, Yuchao Li, Donglin Zhuang, Zhongzhu Zhou, Xiafei Qiu, Yong Li, Wei Lin, Shuaiwen Leon Song		Github Paper
A Simple and Effective Pruning Approach for Large Language Models Mingjie Sun, Zhuang Liu, Anna Bair, J. Zico Kolter		Github Paper
Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, Danqi Chen		Github Paper
Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models Yingtao Zhang, Haoli Bai, Haokun Lin, Jialin Zhao, Lu Hou, Carlo Vittorio Cannistraci		Github Paper
Fluctuation-based Adaptive Structured Pruning for Large Language Models Yongqi An, Xu Zhao, Tao Yu, Ming Tang, Jinqiao Wang		Github Paper
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models Jongwoo Ko, Seungjoon Park, Yujin Kim, Sumyeong Ahn, Du-Seong Chang, Euijai Ahn, Se-Young Yun		Github Paper
LoRAPrune: Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning Mingyang Zhang, Hao Chen, Chunhua Shen, Zhen Yang, Linlin Ou, Xinyi Yu, Bohan Zhuang		Github Paper
Pruning Large Language Models via Accuracy Predictor Yupeng Ji, Yibo Cao, Jiucai Liu		Paper
Compressing LLMs: The Truth is Rarely Pure and Never Simple Ajay Jaiswal, Zhe Gan, Xianzhi Du, Bowen Zhang, Zhangyang Wang, Yinfei Yang		Paper
Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity Lu Yin, Shiwei Liu, Ajay Jaiswal, Souvik Kundu, Zhangyang Wang		Github Paper
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, Shiwei Liu		Github Paper
Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models Song Guo, Jiahang Xu, Li Lyna Zhang, Mao Yang		Github Paper
Sparse Finetuning for Inference Acceleration of Large Language Models Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin, Dan Alistarh		Github Paper
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models Iman Mirzadeh, Keivan Alizadeh, Sachin Mehta, Carlo C Del Mundo, Oncel Tuzel, Golnoosh Samei, Mohammad Rastegari, Mehrdad Farajtabar		Paper
The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite		Paper
One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models Hang Shao, Bei Liu, Bo Xiao, Ke Zeng, Guanglu Wan, Yanmin Qian		Github Paper
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery Tianyi Chen, Tianyu Ding, Badal Yadav, Ilya Zharkov, Luming Liang		Github Paper
Divergent Token Metrics: Measuring degradation to prune away LLM components -- and optimize quantization Björn Deiseroth, Max Meuer, Nikolas Gritsch, Constantin Eichenberg, Patrick Schramowski, Matthias Aßenmacher, Kristian Kersting		Github Paper
Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models Rocktim Jyoti Das, Liqun Ma, Zhiqiang Shen		Github Paper
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs Yuxin Zhang, Lirui Zhao, Mingbao Lin, Yunyun Sun, Yiwu Yao, Xingjia Han, Jared Tanner, Shiwei Liu, Rongrong Ji		Github Paper
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity Yun Li, Lin Niu, Xipeng Zhang, Kai Liu, Jianchen Zhu, Zhanhui Kang		Paper
PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs Max Zimmer, Megi Andoni, Christoph Spiegel, Sebastian Pokutta		Github Paper
Fast and Optimal Weight Update for Pruned Large Language Models Vladimír Boža		Github Paper
Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning Adib Hasan, Ileana Rugina, Alex Wang		Github Paper
SliceGPT: Compress Large Language Models by Deleting Rows and Columns Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, James Hensman		Github Paper
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference Bowen Zhao, Hannaneh Hajishirzi, Qingqing Cao		Paper
ReLU2 Wins: Discovering Efficient Activation Functions for Sparse LLMs Zhengyan Zhang, Yixin Song, Guanghui Yu, Xu Han, Yankai Lin, Chaojun Xiao, Chenyang Song, Zhiyuan Liu, Zeyu Mi, Maosong Sun		Paper
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes Lucio Dery, Steven Kolawole, Jean-Francois Kagey, Virginia Smith, Graham Neubig, Ameet Talwalkar		Github Paper
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia et al		Github Paper Project
NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models Shengrui Li, Xueting Han, Jing Bai		Paper
Learn To be Efficient: Build Structured Sparsity in Large Language Models Haizhong Zheng, Xiaoyan Bai, Beidi Chen, Fan Lai, Atul Prakash		Paper
Shortened LLaMA: A Simple Depth Pruning for Large Language Models Bo-Kyeong Kim, Geonmin Kim, Tae-Ho Kim, Thibault Castells, Shinkook Choi, Junho Shin, Hyoung-Kyu Song		Github Paper
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks Jiwon Song, Kyungseok Oh, Taesu Kim, Hyungjun Kim, Yulhwa Kim, Jae-Joon Kim		Github Paper
HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference Yashas Samaga B L, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli		Paper
LaCo: Large Language Model Pruning via Layer Collapse Yifei Yang, Zouying Cao, Hai Zhao		Paper
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li et al		Github Paper [Model-7B] [Model-13B]
EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs Song Guo, Fan Wu, Lei Zhang, Xiawu Zheng, Shengchuan Zhang, Fei Chao, Yiyu Shi, Rongrong Ji		Github Paper
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation Peng Xu, Wenqi Shao, Mengzhao Chen, Shitao Tang, Kaipeng Zhang, Peng Gao, Fengwei An, Yu Qiao, Ping Luo		Github Paper
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, Weipeng Chen		Paper
Efficient Pruning of Large Language Model with Adaptive Estimation Fusion Jun Liu, Chao Wu, Changdi Yang, Hao Tang, Haoye Dong, Zhenglun Kong, Geng Yuan, Wei Niu, Dong Huang, Yanzhi Wang		Paper
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie et al		Github Paper Project
Compressing Large Language Models by Streamlining the Unimportant Layer Xiaodong Chen, Yuxuan Hu, Jing Zhang		Paper
Multilingual Brain Surgeon: Large Language Models Can be Compressed Leaving No Language Behind Hongchuan Zeng, Hongshen Xu, Lu Chen, Kai Yu		Github Paper
Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy Yijin Liu, Fandong Meng, Jie Zhou		Github Paper
LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models Guangyan Li, Yongqiang Tang, Wensheng Zhang		Github Paper
CATS: Contextually-Aware Thresholding for Sparsity in Large Language Models Je-Yong Lee, Donghyun Lee, Genghan Zhang, Mo Tiwari, Azalia Mirhoseini		Paper
Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer et al		Paper
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment Abhinav Agarwalla, Abhay Gupta, Alexandre Marques, Shubhra Pandit, Michael Goin, Eldar Kurtic, Kevin Leong, Tuan Nguyen, Mahmoud Salem, Dan Alistarh, Sean Lie, Mark Kurtz		Paper
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models Zhiyu Guo, Hidetaka Kamigaito, Taro Wanatnabe		Paper
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, Adam Santoro		Paper
LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models Juan Pablo Munoz, Jinjie Yuan, Yi Zheng, Nilesh Jain		Github Paper
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search Juan Pablo Munoz, Jinjie Yuan, Nilesh Jain		Github Paper
Pruning as a Domain-specific LLM Extractor Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen		Github Paper
Language-Specific Pruning for Efficient Reduction of Large Language Models Maksym Shamrai		Github Paper
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning Dan Qiao, Yi Su, Pinzheng Wang, Jing Ye, Wenjing Xie et al		Github Paper
FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models Yang Zhang, Yawei Li, Xinpeng Wang, Qianli Shen, Barbara Plank, Bernd Bischl, Mina Rezaei, Kenji Kawaguchi		Paper
SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs Mohammad Mozaffari, Amir Yazdanbakhsh, Zhao Zhang, Maryam Mehri Dehnavi		Github Paper
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models Xudong Lu, Aojun Zhou, Yuhui Xu, Renrui Zhang, Peng Gao, Hongsheng Li		Github Paper
Large Language Model Pruning Hanjuan Huang, Hao-Jia Song, Hsing-Kuo Pao		Paper
Effective Interplay between Sparsity and Quantization: From Theory to Practice Simla Burcu Harma, Ayan Chakraborty, Elizaveta Kostenok, Danila Mishin, Dongho Ha, Babak Falsafi, Martin Jaggi, Ming Liu, Yunho Oh, Suvinay Subramanian, Amir Yazdanbakhsh		Paper
VTrans: Accelerating Transformer Compression with Variational Information Bottleneck based Pruning Oshin Dutta, Ritvik Gupta, Sumeet Agarwal		Paper
Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters Yixin Song, Haotong Xie, Zhengyan Zhang, Bo Wen, Li Ma, Zeyu Mi, Haibo Chen		Paper Model
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models Peijie Dong, Lujun Li, Zhenheng Tang, Xiang Liu, Xinglin Pan, Qiang Wang, Xiaowen Chu		Github Paper
MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbations Zixiao Wang, Jingwei Zhang, Wenqian Zhao, Farzan Farnia, Bei Yu		Github Paper
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models Xiang Meng, Kayhan Behdin, Haoyue Wang, Rahul Mazumder		Paper
Optimization-based Structural Pruning for Large Language Models without Back-Propagation Yuan Gao, Zujing Liu, Weizhong Zhang, Bo Du, Gui-Song Xia		Paper
ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models Yash Akhauri, Ahmed F AbouElhamayed, Jordan Dotzel, Zhiru Zhang, Alexander M Rush, Safeen Huda, Mohamed S Abdelfattah		Github Paper
Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization Sungbin Shin, Wonpyo Park, Jaeho Lee, Namhoon Lee		Paper
Learning Neural Networks with Sparse Activations Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka		Paper
FoldGPT: Simple and Effective Large Language Model Compression Scheme Songwei Liu, Chao Zeng, Lianqiang Li, Chenqian Yan, Lean Fu, Xing Mei, Fangmin Chen		Paper
Structured Pruning for Large Language Models Using Coupled Components Elimination and Minor Fine-tuning Honghe Zhang, XiaolongShi XiaolongShi, Jingwei Sun, Guangzhong Sun		Paper
BlockPruner: Fine-grained Pruning for Large Language Models Longguang Zhong, Fanqi Wan, Ruijun Chen, Xiaojun Quan, Liangzhi Li		Github Paper
Flextron: Many-in-One Flexible Large Language Model Ruisi Cai, Saurav Muralidharan, Greg Heinrich, Hongxu Yin, Zhangyang Wang, Jan Kautz, Pavlo Molchanov		Paper
Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations Bowen Shen, Zheng Lin, Daren Zha, Wei Liu, Jian Luan, Bin Wang, Weiping Wang		Github Paper
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression Zhichao Xu, Ashim Gupta, Tao Li, Oliver Bentham, Vivek Srikumar		Github Paper
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated Hongyu Wang, Shuming Ma, Ruiping Wang, Furu Wei		Paper
Reconstruct the Pruned Model without Any Retraining Pingjie Wang, Ziqing Fan, Shengchao Hu, Zhe Chen, Yanfeng Wang, Yu Wang		Paper
MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi		Paper
Compact Language Models via Pruning and Knowledge Distillation Saurav Muralidharan, Sharath Turuvekere Sreenivas, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov		Github Paper
Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining Jianwei Li, Yijun Dong, Qi Lei		Paper
Pruning Large Language Models with Semi-Structural Adaptive Sparse Training Weiyu Huang, Guohao Jian, Yuezhou Hu, Jun Zhu, Jianfei Chen		Paper
A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models Pengxiang Zhao, Hanyu Hu, Ping Li, Yi Zheng, Zhefeng Wang, Xiaoming Yuan		Paper
Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism Guanchen Li, Xiandong Zhao, Lian Liu, Zeping Li, Dong Li, Lu Tian, Jie He, Ashish Sirasao, Emad Barsoum		Paper
LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models Yupeng Su, Ziyi Guan, Xiaoqun Liu, Tianlai Jin, Dongkuan Wu, Graziano Chesi, Ngai Wong, Hao Yu		Github Paper
LLM Pruning and Distillation in Practice: The Minitron Approach Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov		Paper
Language-specific Calibration for Pruning Multilingual Language Models Simon Kurz, Zhixue Zhao, Jian-Jia Chen, Lucie Flek		Paper
PAT: Pruning-Aware Tuning for Large Language Models Yijiang Liu, Huanrui Yang, Youxin Chen, Rongyu Zhang, Miao Wang, Yuan Du, Li Du		Github Paper
STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning Jaeseong Lee, seung-won hwang, Aurick Qiao, Daniel F Campos, Zhewei Yao, Yuxiong He		Paper
Evaluating the Impact of Compression Techniques on Task-Specific Performance of Large Language Models Bishwash Khanal, Jeffery M. Capone		Paper
KVPruner: Structural Pruning for Faster and Memory-Efficient Large Language Models Bo Lv, Quan Zhou, Xuanang Ding, Yan Wang, Zeming Ma		Paper
OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition Stephen Zhang, Vardan Papyan		Paper
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information Yuxin Wang, Minghua Ma, Zekun Wang, Jingchang Chen, Huiming Fan, Liping Shan, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin		Github Paper
Search for Efficient Large Language Models Xuan Shen, Pu Zhao, Yifan Gong, Zhenglun Kong, Zheng Zhan, Yushu Wu, Ming Lin, Chao Wu, Xue Lin, Yanzhi Wang		Paper
MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models Gongfan Fang, Hongxu Yin, Saurav Muralidharan, Greg Heinrich, Jeff Pool, Jan Kautz, Pavlo Molchanov, Xinchao Wang		Github Paper
SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models Juan Pablo Munoz, Jinjie Yuan, Nilesh Jain		Github Paper
Mitigating Copy Bias in In-Context Learning through Neuron Pruning Ameen Ali, Lior Wolf, Ivan Titov		Paper
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, Ajay Kumar Jaiswal, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu		Github Paper
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models David Hoffmann, Kailash Budhathoki, Matthaeus Kleindessner		Paper
DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models Shangqian Gao, Chi-Heng Lin, Ting Hua, Tang Zheng, Yilin Shen, Hongxia Jin, Yen-Chang Hsu		Paper
Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix Yingyu Liang, Jiangxuan Long, Zhenmei Shi, Zhao Song, Yufa Zhou		Paper
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang		Github Paper
Self-Data Distillation for Recovering Quality in Pruned Large Language Models Vithursan Thangarasa, Ganesh Venkatesh, Nish Sinnadurai, Sean Lie		Paper
Self-calibration for Language Model Quantization and Pruning Miles Williams, George Chrysostomou, Nikolaos Aletras		Paper
Beware of Calibration Data for Pruning Large Language Models Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang		Paper
Pruning Foundation Models for High Accuracy without Retraining Pu Zhao, Fei Sun, Xuan Shen, Pinrui Yu, Zhenglun Kong, Yanzhi Wang, Xue Lin		Github Paper
FedSpaLLM: Federated Pruning of Large Language Models Guangji Bai, Yijiang Li, Zilinghan Li, Liang Zhao, Kibaek Kim		Paper
EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh		Github Paper
Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs Kang Zhao, Tao Yuan, Han Bao, Zhenfeng Su, Chang Gao, Zhaofeng Sun, Zichen Liang, Liping Jing, Jianfei Chen		Paper
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment Ge Yang, Changyi He, Jinyang Guo, Jianyu Wu, Yifu Ding, Aishan Liu, Haotong Qin, Pengliang Ji, Xianglong Liu		Github Paper
Tailored-LLaMA: Optimizing Few-Shot Learning in Pruned LLaMA Models with Task-Specific Prompts Danyal Aftab, Steven Davy		Paper
Sparsing Law: Towards Large Language Models with Greater Activation Sparsity Yuqi Luo, Chenyang Song, Xu Han, Yingfa Chen, Chaojun Xiao, Zhiyuan Liu, Maosong Sun		Github Paper
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis Zichen Song, Yuxin Wu, Sitan Huang, Zhongfeng Kang		Paper
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization Hexuan Deng, Wenxiang Jiao, Xuebo Liu, Min Zhang, Zhaopeng Tu		Github Paper
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment Yonggan Fu, Zhongzhi Yu, Junwei Li, Jiayi Qian, Yongan Zhang, Xiangchi Yuan, Dachuan Shi, Roman Yakunin, Yingyan Celine Lin		Github Paper
Scaling Law for Post-training after Model Pruning Xiaodong Chen, Yuxuan Hu, Jing Zhang, Xiaokang Zhang, Cuiping Li, Hong Chen		Paper
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Zichen Song, Sitan Huang, Yuxin Wu, Zhongfeng Kang		Paper
Reassessing Layer Pruning in LLMs: New Insights and Methods Yao Lu, Hao Cheng, Yujie Fang, Zeyu Wang, Jiaheng Wei, Dongwei Xu, Qi Xuan, Xiaoniu Yang, Zhaowei Zhu		Github Paper
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs Akhiad Bercovich, Tomer Ronen, Talor Abramovich, Nir Ailon, Nave Assaf, Mohammad Dabbah et al		Paper
Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking Marco Federici, Davide Belli, Mart van Baalen, Amir Jalalirad, Andrii Skliar, Bence Major, Markus Nagel, Paul Whatmough		Paper