This whitepaper provides an overview of transformer-based architectures and their impact on modern artificial intelligence. It explains how transformers differ from traditional sequential models by using attention mechanisms to process data in parallel. The paper discusses applications of transformers across natural language processing, computer vision, and multimodal AI tasks. It highlights benefits such as improved scalability, contextual understanding, and performance on large datasets. The whitepaper also addresses computational requirements and training considerations, positioning transformers as a foundational technology behind many state-of-the-art AI systems used today.

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