LLM Engineering11 min read• Published July 20, 2026

Building Large Language Models: Tokenization (BPE/WordPiece), RoPE Embeddings, Self-Attention & Transformer Blocks

The inner mechanics of modern decoder-only language models: tokenizers, rotary embeddings, QKV attention math, RMSNorm, and SwiGLU.

MT
Manosakthi Thiyagarajan
Founder & Lead AI Architect

⚡ Executive Summary

Uncover how LLMs are engineered: Byte-Pair Encoding, Rotary Position Embeddings (RoPE), Self-Attention QKV matrices, RMSNorm, and SwiGLU activations.

Key Takeaways

  • ✓Tokenization — covered in depth with practical examples, formulas, and code.
  • ✓Embeddings & Positional Information (RoPE) — covered in depth with practical examples, formulas, and code.
  • ✓Self-Attention Mechanism — covered in depth with practical examples, formulas, and code.

Building LLMs & Defining Parameters — Foundations Guide

At its core, an LLM is a Transformer neural network trained to predict the next token in a sequence, given all previous tokens.

text
Text → Tokenizer → Token IDs → Embeddings → Transformer Blocks (×N) → Output Probabilities → Sampled Token

#1. Tokenization

Splits text into subword units using BPE (Byte-Pair Encoding), WordPiece, or SentencePiece.

  • Example: "unbelievable" → ["un", "believ", "able"].
  • vocab_size: 32K–128K+ tokens in modern models.

    • #2. Embeddings & Positional Information (RoPE)

    Token IDs map to vectors of shape [vocab_size, d_model]. Position is injected via:

  • RoPE (Rotary Position Embedding): Rotates query/key vectors based on sequence position, generalizing to long sequences better than absolute embeddings.

    • #3. Self-Attention Mechanism

    For every token, computes Query (Q), Key (K), and Value (V) vectors:

    MATHEMATICAL FORMULA Attention(Q, K, V) = softmax≤ft( (Q Kᵀ) / (√(dₖ)) right) V

  • Multi-Head Attention: Runs parallel attention heads attending to different syntactic/semantic relationships.
  • Causal Masking: Enforces autoregressive token generation by masking future positions.

    • #4. The Transformer Block

    Stacked layers containing:

  • RMSNorm: Faster normalization skipping mean-centering.
  • SwiGLU FFN: Gated Swish feed-forward network storing parameter knowledge capacity.

    • MATHEMATICAL FORMULA RMSNorm(x) = (x) / (√(frac1)d) ∑ (i=1)^d xᵢ² + ε odot γ

    Tags:#LLM#Tokenization#RoPE#Self-Attention#Transformer#RMSNorm#SwiGLU
    ABOUT THE AUTHOR
    MT

    Manosakthi Thiyagarajan

    Founder & Lead AI Architect

    Manosakthi Thiyagarajan is part of the Junglans Solutions engineering team, specializing in llm engineering. Junglans builds a 20-product ecosystem of local-first enterprise software — AI developer tools, encrypted communication, and data infrastructure with zero cloud telemetry.

    Meet the full Junglans engineering team ↗
    RELATED RESOURCES & REFERENCES

    This article is part of the Junglans Research knowledge base, produced alongside the engineering teams that build our production AI tools. Explore related product documentation and research:

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