LLMs (Part-02): Transformer Encoder Stack

2026-06-27 · Source: Deep Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

This article provides a deep dive into the Transformer Encoder Stack, a core component of Large Language Models, as introduced in the 2017 paper. It details the 6-block encoder's internal processing, starting with key terminologies like input sequence, token, embedding, hidden state, and output state. The encoder's operations involve multi-head self-attention, which calculates relevance scores between all tokens, and a feed-forward network. Multi-head attention, exemplified by Google engineers' choice of 8 heads, captures diverse information such as style, emotionality, and meaning in parallel. Each block concludes with residual addition (Add) and layer normalization (Norm) operations. Residual addition preserves previous features and aids gradient flow during backpropagation, while layer normalization stabilizes the training process by scaling hidden states to a stable range.

Key takeaway

For AI Engineers designing or optimizing Transformer-based models, understanding the encoder's internal mechanics is crucial for performance tuning. You should meticulously configure multi-head attention to capture specific data nuances and ensure residual connections are correctly implemented to prevent vanishing gradients in deep architectures. Leverage layer normalization to maintain training stability, especially when working with complex input sequences or scaling model depth.

Key insights

The Transformer Encoder Stack uses multi-head self-attention and feed-forward networks, enhanced by residual connections and layer normalization, for robust feature extraction.

Principles

• Self-attention maps token relevance.
• Multi-head attention captures diverse nuances.
• Residual connections preserve features and aid gradients.

Method

The encoder processes input through 6 blocks, each performing multi-head self-attention and a feed-forward network, followed by residual addition and layer normalization to produce a final vector embedding.

In practice

• Use 8 attention heads for diverse feature capture.
• Implement residual connections for deep networks.
• Apply layer normalization to stabilize training.

Topics

• Transformer Architecture
• Encoder Stack
• Multi-head Self-attention
• Feed-forward Networks
• Residual Connections
• Layer Normalization
• Large Language Models

Best for: Machine Learning Engineer, AI Engineer, AI Student

Related on AIssential

• Understanding The Encoder (Part II) — The Transformer encoder converts input tokens into rich contextualized representations using multi-head self-attention and positional encodings.
• LLMs (Part-01): The High-level Architecture of Transformers — The Transformer architecture, detailed in "Attention is All You Need," forms the basis for modern LLMs through its encoder-decoder structure and attention mechanisms.
• Inside the Transformer: Attention Mechanisms Deep Dive — Transformers achieve deep learning stability and expressive power through specific architectural choices like residual connections and layer normalization.
• Understanding Transformers, the MLE Way — Transformers use attention to process sequences non-sequentially, enabling faster and more efficient deep learning for various tasks.
• Stop Treating Attention as Magic: How Transformers Actually Work — Transformers process language by letting every token directly decide which other tokens matter.
• Ful LLM / Deep Learning roadmap. — Transformers leverage attention mechanisms and positional encodings to process sequences bidirectionally or causally.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.