Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list for Efficient Large Language Models
| Date | Stars |
|---|---|
| 2026-07-31 | 2029 |
| 2026-08-06 | 2032 |
Today
+3 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Awesome-Efficient-LLM A curated list for **Efficient Large Language Models** ## Full List - [Network Pruning / Sparsity](pruning.md) - [Knowledge Distillation](knowledge_distillation.md) - [Quantization](quantization.md) - [Inference Acceleration](inference_acceleration.md) - [Efficient MOE](efficient_moe.md) - [Efficient Architecture of LLM](efficient_architecture_llm.md) - [KV Cache Compression](kv_cache_compression.md) - [Text Compression](text_compression.md) - [Low-Rank Decomposition](low_rank_decomposition.md) - [Hardware / System / Serving](hardware.md) - [Efficient Fine-tuning](tuning.md) - [Efficient Training](efficient_training.md) - [Survey or Benchmark](survey.md) - [Reasoning Model](https://github.com/fscdc/Awesome-Efficient-Reasoning-Models) ### 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. #### 🚀 Updates * April 15, 2025: We have a new [curated list](https://github.com/fscdc/Awesome-Efficient-Reasoning-Models) for **efficient reasoning model**! * May 29, 2024: We've had this awesome list for a year now :smiling_face_with_three_hearts:! * Sep 6, 2023: Add a new subdirectory [project/](project/) to organize efficient LLM projects. * July 11, 2023: A new subdirectory [efficient_plm/](efficient_plm/) is created to house papers that are applicable to PLMs. #### 💮 Contributing 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. #### :star: Recommended Paper For each topic, we have curated a list of recommended papers that have garnered a lot of GitHub stars or citations. ## Paper from Sep 30, 2024 - Now (see Full List from May 22, 2023 [here](#full-list)) ### Quick Link - [Network Pruning / Sparsity](#network-pruning--sparsity) - [Knowledge Distillation](#knowledge-distillation) - [Quantization](#quantization) - [Inference Acceleration](#inference-acceleration) - [Efficient MOE](#efficient_moe) - [Efficient Architecture of LLM](#efficient-architecture-of-llm) - [KV Cache Compression](#kv-cache-compression) - [Text Compression](#text-compression) - [Low-Rank Decomposition](#low-rank-decomposition) - [Hardware / System / Serving](#hardwaresystemserving) - [Efficient Fine-tuning](#efficient-fine-tuning) - [Efficient Training](#efficient-training) - [Survey](#survey-or-benchmark) ### Network Pruning / Sparsity | Title & Authors | Introduction | Links | |:--| :----: | :---:| | [](https://github.com/IST-DASLab/sparsegpt) []() []() <br> :star: [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot](https://github.com/IST-DASLab/sparsegpt) <br> Elias Frantar, Dan Alistarh| <img width="522" alt="image" src="figures/sparsegpt.png"> |[Github](https://github.com/IST-DASLab/sparsegpt) [paper](https://arxiv.org/abs/2301.00774) | [//]: #Recommend | [](https://github.com/horseee/LLM-Pruner) []() []() <br> :star: [LLM-Pruner: On the Structural Pruning of Large Language Models](https://arxiv.org/abs/2305.11627) <br> Xinyin Ma, Gongfan Fang, Xinchao Wang | <img width="561" alt="image" s
Excerpt of 98,017 characters
Read on GitHub
Ma Xinyin · National University of Singapore · Singapore
490
18
Zhmin Zhao · Software Analysis and Intelligence Lab (SAIL) & Lab on Maintenance, Construction and Intelligence of Software (MCIS) · Canada
14
Gongfan Fang · National University of Singapore · Singapore
13
13
Jang-Hyun Kim · Seoul Nat'l University · South Korea
8
5
5
4
4
4
4
4
3
Piotr Nawrot · United Kingdom
3
2
2
Yuan Feng
2
1
Qingquan Song
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dadcc4b6379a961e, topic:llm, topic:language-model
matched fp:dadcc4b6379a961e, topic:knowledge-distillation