Efficient-ML/Awesome-Model-Quantization

The repository organizes quantization research across benchmarks (BiBench, MQBench), comprehensive surveys on binarization and quantization methods, and chronologically-indexed papers spanning 2015-2024. It covers specialized domains including binary neural networks, spiking neural networks, hardware deployment optimization, and recent advances in LLM/diffusion model quantization. The curated collection enables researchers to track methodological evolution while cross-referencing implementations, empirical comparisons, and deployment-specific solutions.

2,333 stars. Actively maintained with 6 commits in the last 30 days.