NyunAI and Transmute AI Lab jointly announce large model compression method: parameterization based on reduced-order modeling

Recently.NyunAI with Transmute AI Lab has published a joint research paper on the Arxiv page revealing a novel approach to large model compression. This approach is based on the parameterization of reduced-order modeling and provides an effective solution for large model compression.

The core of the method is to perform a low-order decomposition in the feature space and reparameterize it in the weight space. This process allows large models to be efficiently compressed while maintaining the performance of their original models. It is worth noting that this compression technique operates in a hierarchical manner and does not rely on GPU devices. Under tight memory and time constraints, the method is able to successfully compress models of one billion in size.

The principle, implementation details and experimental results of the method are elaborated in the paper. By comparing with the current state-of-the-art structural pruning methods, the method demonstrates excellent efficacy. It provides new ideas and directions for the compression of large models and is expected to promote the development of the model compression field.

This research result is of great significance for application scenarios that need to process large-scale datasets and run complex models. By compressing large models, the demand for computational resources can be reduced, and the running efficiency of the models can be improved, bringing better performance to practical applications.

NyunAI and Transmute AI Lab jointly announced a large model compression method based on the parameterization of reduced-order modeling, which provides an efficient and feasible solution for large model compression. This research will advance the field of model compression and bring more possibilities for practical applications.

Paper Address:https://arxiv.org/pdf/2312.07046.pdf