For Heads-of · Practitioner
Transformer
A deep learning architecture that uses self-attention mechanisms to process sequences of data, foundational to modern large language models.
- transformers
- attention
- architecture
- LLM
How It Works
Transformers process all tokens in parallel rather than sequentially, and use attention weights to determine which parts of the input are most relevant to each output token.
Efficiency
The parallel processing makes transformers faster and more scalable than previous architectures like RNNs (recurrent neural networks) on large datasets.
Dominance in LLMs
All major large language models (GPT, Claude, Gemini, LLaMA) are built on transformer architectures, making this the foundational technology for modern generative AI.