awesome-model-quantization
This repository serves as an extensive resource on model quantization, collecting essential papers, documents, and codes for researchers in the field. It features continuous updates and includes topics like network binarization and benchmarking with BiBench and MQBench, as well as comprehensive surveys on quantization methods and binary neural networks. Highlighting the 'Awesome Efficient AIGC' initiative, the project focuses on contemporary techniques for compressing and speeding up large language and diffusion models. Contributions are welcomed to enhance the breadth and utility of this repository.
