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Transformer Architecture

Stacks of self-attention and position-wise feedforward blocks with residual connections and normalization, processing all positions in parallel with order injected via positional information. Recurrence is eliminated entirely, which is exactly what makes training parallelizable.

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Transformer Architecture and Next Token Prediction in Large Language Models

Transformers utilize a hierarchical neural network architecture where input data is tokenized and embedded into high-dimensional vector spaces that encode semantic meaning via continuous parameters w…