First, Understand What It Does
Transformer is a neural network architecture that uses operations like attention to exchange information between different positions in the input.
Understanding with an Example
In the sentence "Put the textbook in the bag because it is too heavy," the "it" could refer to different things. The model needs to consider the relationships between multiple words in context, rather than looking only at adjacent characters. Attention is one mechanism that enables this kind of information interaction.
With position information
Multi-layer information interaction and transformation
Used for prediction and other tasks
Going a Step Further
The original Transformer consists of an encoder and a decoder. Many autoregressive language models adopt a decoder-style architecture and use causal masking so that positions can only access previously allowed content. Beyond attention, it also includes feed-forward networks, residual connections, and normalization.
What It Does Not Explain
Transformer is the name of an architecture, not a training phase or an optimizer. Not all AI systems use it; models that share this architecture can still have different objectives and capabilities. The diagram omits implementation details like multi-head attention and multiple layers.
Where to Go Next
Sources
- Attention Is All You Need: Transformer is an attention-centric model architecture; the original paper includes an encoder and a decoder.
- Hugging Face · Caching: In autoregressive inference, historical K/V are reused layer by layer; attention Q/K/V and cache memory overhead.
Verified on 2026-09-09; the original paper is used to illustrate the mechanism, and the example in the text is for teaching purposes.