AI-Efficiency/Awesome-Model-Quantization
A curated collection of papers, benchmarks, surveys, and tools for model quantization, covering low-bit networks, LLMs, multimodal and generative models, vector and lattice quantization, and efficient
- Stars
- 2.5k
- Forks
- 243
- Open issues
- 0
- Last push
- 4d ago
About Awesome-Model-Quantization
Awesome-Model-Quantization is a free, open-source AI resource list by AI-Efficiency. A curated collection of papers, benchmarks, surveys, and tools for model quantization, covering low-bit networks, LLMs, multimodal and generative models, vector and lattice quantization, and efficient. It has 2.5k GitHub stars and 243 forks, and was last updated 4d ago. GitHub doesn't detect a standard license, so check the repository before using it commercially.
- License
- Not detected
- Language
- –
- Stars
- 2,456
- Last update
- 4d ago
- Runs on Mac
- Not stated
- GPU
- Not stated
- Paid API key
- Not stated
- Self-hostable
- Not stated
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Awesome-Model-Quantization FAQ
Is Awesome-Model-Quantization free?
Yes. Awesome-Model-Quantization is open source and free to use. GitHub doesn't detect a standard license, so check the repository before using it commercially.
Does Awesome-Model-Quantization run on a Mac?
The project doesn't say. Check its installation guide before you start.
Does Awesome-Model-Quantization need a GPU?
The project doesn't state hardware needs. AI models generally run much faster with a GPU.
Is Awesome-Model-Quantization still maintained?
Yes. It was last updated 4d ago and has 0 open issues on GitHub.
What are alternatives to Awesome-Model-Quantization?
Similar free, open-source tools include anomaly-detection-resources, KG-LLM-Papers, KnowledgeEditingPapers, Multimodal-AND-Large-Language-Models.