Transformer Architecture
The Transformer architecture, introduced in the 2017 paper "Attention Is All You Need", is the foundation of all modern LLMs. It replaced older RNNs and LSTMs with a purely attention‑based design.
High‑Level Structure
A transformer consists of an encoder (reads input) and a decoder (generates output). For generative tasks like text completion, we often use only the decoder (GPT‑style). For translation, we use both (original Transformer).
Input → Embedding → Multi‑Head Attention → Feed‑Forward → Output (repeated N times)Key Components
- Positional Encoding: Since attention has no sense of order, we add position information to embeddings.
- Multi‑Head Attention: Captures relationships between tokens.
- Feed‑Forward Network: Processes each token independently after attention.
- Residual Connections & Layer Normalization: Help training stability.
Encoder vs. Decoder
- Encoder: Reads entire input at once (bidirectional). Used in BERT for understanding.
- Decoder: Generates one token at a time, using masked attention (cannot see future tokens). Used in GPT for generation.
Why Transformers Won
- Parallelization (unlike sequential RNNs) → faster training on GPUs.
- Captures long‑range dependencies better.
- Scales well with data and compute (the "bitter lesson").
Two Minute Drill
- Transformer uses attention only, no recurrence.
- Encoder for understanding, decoder for generation.
- Key parts: positional encoding, multi‑head attention, feed‑forward.
- Transformers enabled large scale and parallel training.
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