Transformer Architecture
The Transformer architecture models relationships using attention alongside learned transformations and positional information. It underpins encoder, decoder and encoder–decoder systems for many tasks. The skill is understanding attention masks, representation flow and runtime costs, so the selected architecture and input format match what information is available when a prediction is made.
What it is
Attention forms weighted combinations of value representations using relationships between queries and keys. Multiple heads learn different projection spaces, while feed-forward blocks, residual connections and normalization transform and stabilize representations. Positional information gives the model access to order that attention alone does not encode. Encoders can attend across an input, causal decoders restrict attention to preceding positions and encoder–decoder models connect an input representation to generated output. These variants have different task and leakage implications. KV caching reuses prior key and value computations during suitable autoregressive inference, but is an execution mechanism rather than the defining concept of a Transformer.
What the work involves
Choose encoder, causal decoder or encoder–decoder structure from the task. Verify masks, positional handling, padding and token alignment before training or inference. Inspect context-length and memory behavior and compare attention implementations only with behavioral checks. For generation, understand cache state and how decoding settings interact with the model. The deliverable should document architecture and input contracts with evaluation on relevant sequences, including evidence that future or padded information is not unintentionally available and that runtime optimization preserves the intended prediction behavior.
Illustrative example
For an illustrative language-understanding classifier, an engineer uses a bidirectional encoder over complete messages. A separate next-token experiment instead applies a causal mask, preventing access to future tokens. The engineer checks both tasks on short synthetic sequences where information flow is easy to inspect. During autoregressive inference, caching reduces repeated computation, but cached and uncached outputs are compared under controlled settings before the optimization is accepted.
Limits and common mistakes
Attention weights are not automatically faithful explanations or causal contributions. Context size and memory grow with architecture and implementation, and position handling may weaken on lengths unlike training. Incorrect masks can produce excellent but leaked results. Transformer architecture is distinct from a particular pretrained language model and does not imply generation, reasoning or multimodal support by itself. Check information flow and the full input-processing contract, and assess optimized attention or caching under the exact workload rather than relying on architectural labels.
Prerequisites
- hardDeep Learning
Self-attention, layer normalization, residual connections, softmax — all are DL building blocks assembled in the Transformer
- hardLinear Algebra
Q·Kᵀ/√d is a scaled dot product of matrices; multi-head attention is parallel matrix projections — Transformers ARE linear algebra in action
Related skills
- → is subcategory of: Deep Learning
- ← is subcategory of: BERT
Sources and further reading
- Attention Is All You Need
Multi-head attention, positional encoding and encoder–decoder architecture.
Last updated: 2026-10-10