Transformer-based Neural Network Architecture

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A Transformer-based Neural Network Architecture is a feedforward deep sequential data neural network architecture based on Transformer blocks.



References

2023

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    • A Transformer Model Architecture, on the other hand, is a blueprint or template for building Transformer-based neural networks. It defines the overall structure and components of the network, including the arrangement of transformer blocks, self-attention mechanisms, feed-forward layers, and other architectural details. The architecture serves as a foundation for creating specific neural network models with different configurations, hyperparameters, and training data.

      Example: The GPT (Generative Pre-trained Transformer) architecture is a Transformer Model Architecture. It consists of a decoder-only structure composed of a stack of transformer blocks. The architecture can be used to create various Transformer-based neural networks for different tasks, such as language modeling and text generation. GPT-3 is one of the models based on the GPT architecture, and the "Davinci" model is a specific instance within the GPT-3 family.