Building a Small Language Model (1) — Understanding Transformer

2026-03-13 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

This article provides a foundational understanding of Large Language Model (LLM) architecture, detailing the full training pipeline from text input to token generation. It explains the role of tokenizers, emphasizing subword granularity and the Byte-Pair Encoding (BPE) algorithm to manage vocabulary size and Out-of-Vocabulary (OOV) issues. The piece then covers embedding, which converts discrete token IDs into continuous, high-dimensional semantic vectors, and introduces positional encoding, including Rotary Position Embedding (RoPE), to inject sequence order information. The core Transformer Block is analyzed, breaking down Self-Attention for contextual understanding, Feed-Forward Networks for knowledge storage, and the importance of Residual Connections and normalization techniques like RMSNorm for stable training. Finally, it describes the LM Head and Softmax sampling for predicting the next token in an autoregressive generation process.

Key takeaway

For AI Engineers building or optimizing LLMs, understanding the detailed mechanics of tokenization, embedding, and the Transformer Block is crucial. Your choice of tokenizer (e.g., BPE) directly impacts VRAM usage and inference speed, while positional encoding (e.g., RoPE) and normalization (e.g., RMSNorm) significantly affect model performance and training stability. Prioritize efficient tokenization and modern architectural components to enhance both training and inference efficiency.

Key insights

LLMs predict the next token by processing numerical representations of text through a multi-stage Transformer architecture.

Principles

• Subword tokenization balances vocabulary size and OOV handling.
• Embeddings convert discrete tokens into continuous semantic vectors.
• Self-Attention enables contextual understanding across tokens.

Method

The LLM pipeline involves tokenization (BPE), embedding with positional encoding (RoPE), processing through stacked Transformer Blocks (Self-Attention, FFN), and final token prediction via LM Head and Softmax sampling.

In practice

• Use subword tokenization to control vocabulary size.
• Implement RoPE for improved context extrapolation.
• Apply RMSNorm for faster, more stable model training.

Topics

• Large Language Models
• Transformer Architecture
• Text Tokenization
• Positional Encoding
• Self-Attention Mechanism

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

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