Transformers

What are Transformers?

The Transformer is a neural network architecture introduced in the 2017 paper "Attention Is All You Need" by Vaswani et al. It replaced recurrent architectures (RNNs, LSTMs) as the dominant approach for sequence modeling by relying entirely on self-attention mechanisms.

Why Transformers Matter

Before Transformers, sequence models processed tokens one at a time (sequentially). This made training slow and limited the model's ability to capture long-range dependencies. Transformers solve both problems by processing all tokens in parallel and using attention to relate any token to any other token regardless of distance.

Core Components

Self-Attention

The key innovation. For each token in the input, the model computes how much "attention" it should pay to every other token. This is done through three learned projections: Query (Q), Key (K), and Value (V).

The attention score is computed as:

Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) V

Where d_k is the dimension of the key vectors, used for scaling.

Multi-Head Attention

Instead of computing a single attention function, Transformers use multiple "heads" - each learning different attention patterns. The outputs are concatenated and projected back to the model dimension.

Position Encoding

Since Transformers process all tokens in parallel, they have no inherent sense of order. Positional encodings (sinusoidal or learned) are added to the input embeddings to provide position information.

Feed-Forward Networks

After attention, each token passes through a two-layer feed-forward network (applied independently to each position). This adds non-linearity and increases the model's representational capacity.

Encoder vs Decoder

• Encoder-only (BERT): Bidirectional attention. Good for classification, NER, embeddings.
• Decoder-only (GPT): Causal (left-to-right) attention. Good for generation.
• Encoder-Decoder (T5, BART): Full architecture. Good for translation, summarization.

Key Variants

Model	Type	Key Innovation
BERT	Encoder	Masked language modeling
GPT	Decoder	Autoregressive pre-training
T5	Enc-Dec	Text-to-text framework
LLaMA	Decoder	Efficient open-source LLM
Mistral	Decoder	Sliding window attention

Why This Matters for Enterprise AI

Transformers are the foundation of every modern LLM deployment. Understanding the architecture is essential for making informed decisions about model selection, fine-tuning strategies, and inference optimization in production systems.