Transformers
The Transformer architecture, introduced in the 2017 paper "Attention Is All You Need", revolutionized deep learning. It relies entirely on self‑attention mechanisms, removing recurrence and convolution. Transformers are the backbone of modern LLMs (GPT, BERT, Llama).
Transformer = encoder‑decoder architecture with multi‑head self‑attention and feed‑forward networks.
Key Components
- Self‑attention: each token attends to all tokens in the sequence, capturing dependencies regardless of distance.
- Multi‑head attention: multiple attention heads in parallel, each learning different relationships.
- Positional encoding: adds information about token position because self‑attention is permutation‑invariant.
- Feed‑forward network: per‑token MLP after attention.
- Layer normalization + residual connections: stabilize training.
Encoder and Decoder
Encoder: processes input sequence (e.g., English sentence). Decoder: generates output sequence (e.g., French translation). In generative models like GPT, only decoder is used (with masked self‑attention to prevent looking ahead).
Why Transformers Succeeded
- Parallelizable (unlike RNNs) → trains faster on GPUs.
- Captures long‑range dependencies better than RNNs.
- Scales well with data and compute (the "bitter lesson").
Variants
- BERT: encoder‑only (masked language modeling).
- GPT: decoder‑only (autoregressive generation).
- T5: encoder‑decoder (text‑to‑text).
- Vision Transformer (ViT): applies transformer to image patches.
Two Minute Drill
- Transformers use self‑attention, not recurrence.
- Multi‑head attention, positional encoding, feed‑forward layers.
- Encoder for understanding, decoder for generation.
- Foundation of BERT, GPT, Llama, and most modern LLMs.
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