No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by HuangOwen · Agent Tool · ★ 1.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-LLM-Compression safe to install? View the security audit →
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 A Survey on Model Compression for Large Language Models TACL [[Paper]](https://arxiv.org/abs/2308.07633) The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models EMNLP 2023 [[Paper]](https://arxiv.org/abs/2312.00960) [[Code]](https://github.com/NamburiSrinath/LLMCompression) The Efficiency Spectrum of Large Language Models: An Algorithmic Survey Arxiv 2023 [[Paper]](https://arxiv.org/abs/2312.00678) Efficient Large Language Models: A Survey TMLR [[Paper]](https://arxiv.org/abs/2312.03863) [[GitHub Page]](https://github.com/AIoT-MLSys-L
| Stars | 1,867 |
| Forks | 130 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 45.41471869336/100 |
| Last Updated | 2026-08-27 |
| Created | 2023-05-30 |
| Est. Tokens | ~28k |
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Awesome-LLM-Compression is Awesome LLM compression research papers and tools.. It is categorized as a Agent Tool with 1.9k GitHub stars.
You can find installation instructions and usage details in the Awesome-LLM-Compression GitHub repository at github.com/HuangOwen/Awesome-LLM-Compression. The project has 1.9k stars and 130 forks, indicating an active community.
Awesome-LLM-Compression is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Awesome-LLM-Compression on Agent Skills Hub include notebooklm-skill, agent-skills, excalidraw-diagram-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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