Awesome-LLM-Compression
Awesome LLM compression research papers and tools to accelerate LLM training and inference.
GraphCanon updated 1w · GitHub synced 1w
Decision brief
Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases.
Good fit when
- When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference.
- If your project requires a detailed survey of model compression for large language models covering various aspects like quantization, pruning, distillation, and efficient prompting.
Avoid when
- Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information.
- If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects.
- Requirements:
- The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (37d since push)
- As of 1w
- Provenance
- Not a fork · Personal account
- As of 1w
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- No lockfile
- As of 1mo
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Install
git clone https://github.com/HuangOwen/Awesome-LLM-CompressionHow it fits your stack(1)
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Overview
Compilation of research papers and tools focused on compressing large language models for improved computational efficiency during both training and serving phases.
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README
Awesome LLM Compression
Awesome LLM compression research papers and tools to accelerate LLM training and inference.
Contents
- 📑 Papers
- Survey
- Quantization
- Pruning and Sparsity
- Distillation
- Efficient Prompting
- KV Cache Compression
- Other
- 🔧 Tools
- 🙌 Contributing
- 🌟 Star History
Papers
Survey
-
Compressed but Compromised? A Study of Jailbreaking in Compressed LLMs
NeurIPS Lock-LLM Workshop 2025 [Paper] [[Blog]] (https://namburisrinath.medium.com/compressed-but-compromised-a-study-of-jailbreaking-in-compressed-llms-02a6e40aaf17) -
A Survey on Model Compression for Large Language Models
TACL [Paper] -
The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models
EMNLP 2023 [Paper] [Code] -
The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
Arxiv 2023 [Paper] -
Efficient Large Language Models: A Survey
TMLR [Paper] [GitHub Page] -
Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
ICML 2024 Tutorial [Paper] [Tutorial] -
Understanding LLMs: A Comprehensive Overview from Training to Inference
Arxiv 2024 [Paper] -
Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
IJCAI 2024 (Survey Track) [Paper] [GitHub Page] -
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Arxiv 2024 [Paper] -
A Survey on Hardware Accelerators for Large Language Models
Arxiv 2024 [Paper] -
A Comprehensive Survey of Compression Algorithms for Language Models
Arxiv 2024 [Paper] -
A Survey on Transformer Compression
Arxiv 2024 [Paper] -
Model Compression and Efficient Inference for Large Language Models: A Survey
Arxiv 2024 [Paper] -
LLM Inference Unveiled: Survey and Roofline Model Insights
Arxiv 2024 [Paper] -
A Survey on Knowledge Distillation of Large Language Models
Arxiv 2024 [Paper] [GitHub Page] -
Efficient Prompting Methods for Large Language Models: A Survey
Arxiv 2024 [Paper] -
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
Arxiv 2024 [Paper] -
On-Device Language Models: A Comprehensive Review
Arxiv 2024 [Paper] [GitHub Page] [Download On-device LLMs] -
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms
Arxiv 2024 [Paper] -
Contextual Com
For agents
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