Transformer Architecture
The Transformer is a neural network architecture built entirely on attention and feed-forward layers, replacing recurrence with self-attention for sequence modeling. It enables parallel training and long-range context.
What is Transformer Architecture?
The Transformer is a neural network architecture built entirely on attention and feed-forward layers, replacing recurrence with self-attention for sequence modeling. It enables parallel training and long-range context.
The Transformer is a neural network architecture built entirely on attention and feed-forward layers, replacing recurrence with self-attention for sequence modeling. It enables parallel training and long-range context.
Where is it used?
GPT, Llama, Claude, BERT, T5, and virtually every modern LLM are transformers; the architecture scaled from the original 65M-parameter model to trillion-parameter frontier models.
How to build it
Stack N transformer blocks (multi-head attention + MLP + LayerNorm + residuals), add token and positional embeddings, and a final LM head — this is the GPT architecture in PyTorch.