Transformer
A transformer is a neural network architecture that uses attention to process sequences (e.g., text or tokens) in parallel rather than step-by-step. It underlies most large language models and many vision and multimodal systems.
In Simple Terms
Think of it as a room where every word can look at every other word at once to decide what to say next.
Detailed Explanation
Transformers treat each position in a sequence as able to attend to every other position, which captures long-range dependencies. They are trained at scale on huge text (and other) corpora and can be fine-tuned for specific tasks. The architecture is the basis for models like GPT, BERT, and Claude. Understanding transformers helps with prompt design, model selection, and reasoning about capabilities and limits. They have largely replaced earlier recurrent and convolutional sequence models in NLP.
Related Terms
Attention Mechanism
The attention mechanism lets a model focus on different parts of its input when producing each part of the output. It is the core of transformer architectures and enables handling long sequences and rich context.
Read moreFine-Tuning
Fine-tuning is training a pre-trained model on your own data so it gets better at specific tasks or styles while keeping its general abilities.
Read moreRetrieval-Augmented Generation (RAG)
RAG is a pattern where an AI model gets up-to-date or private information from a search step before answering, so answers are grounded in your data.
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