Category:
Category:
Transformer
Category:
AI Foundations
Definition
The neural network architecture behind modern large language models.
Explanation
The Transformer architecture uses self-attention to process entire sequences of tokens in parallel. Introduced in 2017, it replaced recurrent neural networks and enabled massive scaling. Transformers are the foundation of modern LLMs, multimodal models, and agentic AI systems.
Technical Architecture
Input Embeddings → Multi-Head Self-Attention → Feedforward Layers → Output Embeddings
Core Component
Self-attention, multi-head attention, positional encoding
Use Cases
Language models, vision transformers, multimodal AI
Pitfalls
High compute cost, memory intensive, scaling challenges
LLM Keywords
Transformer Architecture, Self-Attention
Related Concepts
Related Frameworks
• LLM
• Attention Mechanism
• Embeddings
• PyTorch Transformer
• TensorFlow Transformer