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MMed-RAG: A Versatile Multimodal Retrieval-Augmented Generation System Transforming Factual Accuracy in Medical Vision-Language Models Across Multiple Domains
Nvidia AI has developed a new type of transformer architecture called the Normalized Transformer (nGPT) that uses hypersphere-based normalization.
This innovation significantly speeds up the training process for large language models (LLMs) by 4 to 20 times while also improving stability. The nGPT addresses the challenges of vanishing gradients and unstable training dynamics present in traditional transformer models.
By restricting activations to a hypersphere, it creates a more stable training landscape, leading to faster convergence and better performance.
This breakthrough has the potential to accelerate LLM training and reduce computational costs.
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By Michael IversenNvidia AI has developed a new type of transformer architecture called the Normalized Transformer (nGPT) that uses hypersphere-based normalization.
This innovation significantly speeds up the training process for large language models (LLMs) by 4 to 20 times while also improving stability. The nGPT addresses the challenges of vanishing gradients and unstable training dynamics present in traditional transformer models.
By restricting activations to a hypersphere, it creates a more stable training landscape, leading to faster convergence and better performance.
This breakthrough has the potential to accelerate LLM training and reduce computational costs.