The one-bit (OneBit) large model compression method jointly developed by Tsinghua University and Harbin Institute of Technology has caused huge repercussions in the academic community. This method successfully compresses large models to 1 bit while maintaining 83% performance, breaking through the previous 2 bit limit and providing new possibilities for the deployment of large models on mobile devices. The success of the OneBit method lies not only in its efficient compression rate, but also in its combination of innovative technologies such as 1-bit layer structure, SVID-based parameter initialization and quantization-aware training, which points the way for the lightweight development of future artificial intelligence models.
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The OneBit method jointly proposed by Tsinghua University and Harbin Institute of Technology successfully compressed large models to 1 bit and maintained 83% performance. This method breaks through the past 2-bit limit and adopts 1-bit quantization, which has attracted widespread attention in the academic community. Combining 1-bit layer structure, SVID-based parameter initialization and quantization-aware training, this method breaks new ground. This breakthrough means new possibilities for deploying large models on PCs and smartphones, and is expected to realize the vision of running large models efficiently on mobile devices.The emergence of the OneBit method indicates that future AI models will be more portable and efficient and can be applied on more devices, bringing new opportunities to the popularization and development of artificial intelligence. This breakthrough development deserves continued attention and in-depth research. I believe that more innovative applications based on this will be born in the future.