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 weighted by tunable matrices. The core mechanism relies on attention blocks to dynamically calculate context-dependent relationships between vectors through dot-product based similarity measures, followed by feed-forward networks operating in parallel across all dimensions. This theoretical framework enables the probabilistic generation of sequences by transforming raw inputs into probability distributions over token vocabularies via operations that strictly adhere to tensor algebra and matrix multiplication principles inherent to deep learning.
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…