AI-Efficiency/Awesome-Model-Quantization
A continuously updated collection of papers, benchmarks, and resources on model quantization techniques for compressing and accelerating neural networks and LLMs.
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This repository aggregates academic papers, benchmarks, documentation, and implementations focused on model quantization—a key optimization technique for reducing model size, memory footprint, and computational cost while maintaining accuracy. It includes organized references by year, survey papers, benchmark tools like BiBench, and implementations for binarization and LLM quantization. The repository serves as a reference resource for researchers and practitioners working on efficient deep learning inference.
Frequently asked
- What is AI-Efficiency/Awesome-Model-Quantization?
- A continuously updated collection of papers, benchmarks, and resources on model quantization techniques for compressing and accelerating neural networks and LLMs.
- Is Awesome-Model-Quantization open source?
- Yes — AI-Efficiency/Awesome-Model-Quantization is an open-source project tracked on heatdrop.
- How popular is Awesome-Model-Quantization?
- AI-Efficiency/Awesome-Model-Quantization has 2.4k stars on GitHub.
- Where can I find Awesome-Model-Quantization?
- AI-Efficiency/Awesome-Model-Quantization is on GitHub at https://github.com/AI-Efficiency/Awesome-Model-Quantization.