A Foundation-Expert Paradigm for Hyperscale Model Deployment

View a PDF file from the paper entitled “Realization of the Laws of Specifications in Recommendation Systems

PDF HTML (experimental) view

a summary:While the scaling laws promise great gains in the performance of the recommendation systems, spreading the superior models with efficiency still represents a major challenge that has not been solved. In contrast to the fields where FMS is already adopted on a large scale such as natural language processing and computer vision, progress in recommendation systems is impeded through unique challenges including the need to learn from online broadcast data within the framework of variable data distributions, and ensuring a different recommendation. To bridge this gap, we suggest taking advantage of the foundation-experience model: a framework designed to develop and publish FMERSCAE FMS recommendation. In our approach, the central FM is trained in the data of the multimedia user, multimedia, to learn generalized knowledge. Then this knowledge is efficiently transferred to the various “experts” models, which are lightweight, defined by the targeted perceptions, allowing them to adapt to local data distributions and improvement goals with the minimum public expenditures. To meet our training, inference and development needs, we have built Hypercast, a production -category infrastructure system that reports training, applying, registration and repetition to run this separate model. Our approach is now published in Meta, which serves tens of billions of user requests daily, indicating metropolitan improvements online on the previous stage of the one -stage production system while improving the speed of developers and maintaining infrastructure efficiency. To the extent of our knowledge, this work represents the first successful publication of the foundation model on this range, as it provides an installed and effective plan for the reckoning and a friend of developers to realize the promise to reduce the laws of recommendation.