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Transformer
The foundational neural network architecture behind modern LLMs that processes all words in parallel using self-attention.
Why it exists
Older models (RNNs) processed text word-by-word sequentially. Transformers process entire text sequences in parallel, allowing massive scaling on GPUs.
How it works
Uses stacked encoder/decoder blocks with multi-head self-attention to link related words across long sequences and feed-forward layers to refine representations.
Related terms
Where you'll use this
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