Awesome-Efficient-LLM
A curated list for Efficient Large Language Models
Stars: 1177
Awesome-Efficient-LLM is a curated list focusing on efficient large language models. It includes topics such as knowledge distillation, network pruning, quantization, inference acceleration, efficient MOE, efficient architecture of LLM, KV cache compression, text compression, low-rank decomposition, hardware/system, tuning, and survey. The repository provides a collection of papers and projects related to improving the efficiency of large language models through various techniques like sparsity, quantization, and compression.
README:
A curated list for Efficient Large Language Models
- Network Pruning / Sparsity
- Knowledge Distillation
- Quantization
- Inference Acceleration
- Efficient MOE
- Efficient Architecture of LLM
- KV Cache Compression
- Text Compression
- Low-Rank Decomposition
- Hardware / System
- Tuning
- Survey
- Leaderboard
Please check out all the papers by selecting the sub-area you're interested in. On this main page, only papers released in the past 90 days are shown.
- May 29, 2024: We've had this awesome list for a year now 🥰!
- Sep 6, 2023: Add a new subdirectory project/ to organize efficient LLM projects.
- July 11, 2023: A new subdirectory efficient_plm/ is created to house papers that are applicable to PLMs.
If you'd like to include your paper, or need to update any details such as conference information or code URLs, please feel free to submit a pull request. You can generate the required markdown format for each paper by filling in the information in generate_item.py
and execute python generate_item.py
. We warmly appreciate your contributions to this list. Alternatively, you can email me with the links to your paper and code, and I would add your paper to the list at my earliest convenience.
For each topic, we have curated a list of recommended papers that have garnered a lot of GitHub stars or citations.
Paper from July 13, 2024 - Now (see Full List from May 22, 2023 here)
- Network Pruning / Sparsity
- Knowledge Distillation
- Quantization
- Inference Acceleration
- Efficient MOE
- Efficient Architecture of LLM
- KV Cache Compression
- Text Compression
- Low-Rank Decomposition
- Hardware / System
- Tuning
- Survey
Title & Authors | Introduction | Links |
---|---|---|
⭐ Fast Inference of Mixture-of-Experts Language Models with Offloading Artyom Eliseev, Denis Mazur |
Github Paper |
|
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts Zeliang Zhang, Xiaodong Liu, Hao Cheng, Chenliang Xu, Jianfeng Gao |
Paper |
Title & Authors | Introduction | Links |
---|---|---|
⭐ MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT Omkar Thawakar, Ashmal Vayani, Salman Khan, Hisham Cholakal, Rao M. Anwer, Michael Felsberg, Tim Baldwin, Eric P. Xing, Fahad Shahbaz Khan |
Github Paper Model |
|
⭐ Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length Xuezhe Ma, Xiaomeng Yang, Wenhan Xiong, Beidi Chen, Lili Yu, Hao Zhang, Jonathan May, Luke Zettlemoyer, Omer Levy, Chunting Zhou |
Github Paper |
|
SentenceVAE: Enable Next-sentence Prediction for Large Language Models with Faster Speed, Higher Accuracy and Longer Context Hongjun An, Yifan Chen, Zhe Sun, Xuelong Li |
Paper | |
Efficient LLM Training and Serving with Heterogeneous Context Sharding among Attention Heads Xihui Lin, Yunan Zhang, Suyu Ge, Barun Patra, Vishrav Chaudhary, Xia Song |
Github Paper |
|
Beyond KV Caching: Shared Attention for Efficient LLMs Bingli Liao, Danilo Vasconcellos Vargas |
Github Paper |
Title & Authors | Introduction | Links |
---|---|---|
MoDeGPT: Modular Decomposition for Large Language Model Compression Chi-Heng Lin, Shangqian Gao, James Seale Smith, Abhishek Patel, Shikhar Tuli, Yilin Shen, Hongxia Jin, Yen-Chang Hsu |
Paper |
Title & Authors | Introduction | Links |
---|---|---|
SparseGrad: A Selective Method for Efficient Fine-tuning of MLP Layers Viktoriia Chekalina, Anna Rudenko, Gleb Mezentsev, Alexander Mikhalev, Alexander Panchenko, Ivan Oseledets |
Github Paper |
|
SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching Tianyi Zhang, Junda Su, Oscar Wu, Zhaozhuo Xu, Anshumali Shrivastava |
Paper | |
Bone: Block Affine Transformation as Parameter Efficient Fine-tuning Methods for Large Language Models Jiale Kang |
Github Paper |
|
Enabling Resource-Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines Lei Gao, Amir Ziashahabi, Yue Niu, Salman Avestimehr, Murali Annavaram |
Paper | |
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs Afia Anjum, Maksim E. Eren, Ismael Boureima, Boian Alexandrov, Manish Bhattarai |
Paper |
Title & Authors | Introduction | Links |
---|---|---|
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms Ruihao Gong, Yifu Ding, Zining Wang, Chengtao Lv, Xingyu Zheng, Jinyang Du, Haotong Qin, Jinyang Guo, Michele Magno, Xianglong Liu |
Paper | |
Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey Sourav Verma |
Github Paper |
|
Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview Yanshu Wang, Tong Yang, Xiyan Liang, Guoan Wang, Hanning Lu, Xu Zhe, Yaoming Li, Li Weitao |
Paper | |
Hardware Acceleration of LLMs: A comprehensive survey and comparison Nikoletta Koilia, Christoforos Kachris |
Paper | |
A Survey on Symbolic Knowledge Distillation of Large Language Models Kamal Acharya, Alvaro Velasquez, Houbing Herbert Song |
Paper | |
Inference Optimization of Foundation Models on AI Accelerators Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis |
Paper |
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