Building an LLM From Scratch: The Mechanism That Changed AI Forever, Implemented From Zero

2026-06-10 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Mathematics & Computational Sciences · Depth: Advanced, long

Summary

This article details the implementation of self-attention, a core mechanism in Large Language Models, built from scratch. It explains how self-attention addresses word ambiguity by calculating relevance between words using Query (Q), Key (K), and Value (V) vectors. The process involves dot products, SoftMax normalization, and crucial scaling by √dₖ to prevent vanishing gradients. The author provides Python code for both forward and backward passes, incorporating causal masking to prevent future token leakage. After 15 epochs on 969,000 sequences, the model's loss dropped from 7.6 to 5.4, outperforming random guessing on a 24,000-word vocabulary by 100x. Visualization confirmed that words like "man" (0.508 drift) significantly updated their embeddings, while others like "above" (near zero drift) did not, validating the attention mechanism. The current model, however, lacks positional encoding, residual connections, a feed-forward layer, and multi-head attention, which are essential for a complete Transformer block.

Key takeaway

For Machine Learning Engineers focused on LLM architecture, understanding self-attention's foundational mechanics, including the SoftMax(QKᵀ/√dₖ)V formula and causal masking, is crucial. This deep dive reveals why components like positional encoding and multi-head attention are not optional, guiding your next steps in building a complete Transformer block. Focus on these missing elements to achieve higher accuracy and robust contextual understanding in your models.

Key insights

Self-attention enables LLMs to understand word relevance by dynamically weighting contextual dependencies.

Principles

• Self-attention uses Q, K, V vectors for contextual word representation.
• Scaling QKᵀ by √dₖ stabilizes SoftMax gradients.
• Causal masking prevents future token leakage during training.

Method

Implement self-attention by projecting embeddings into Q, K, V, computing scaled dot products, applying SoftMax, and multiplying by V, followed by backpropagation.

In practice

• Visualize embedding drift to confirm attention mechanism functionality.
• Apply causal masking for autoregressive sequence generation.

Topics

• Self-Attention
• Large Language Models
• Transformer Architecture
• Deep Learning Implementation
• Backpropagation
• Natural Language Processing

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

Related on AIssential

• The Secret Sauce of Modern AI: Self-Attention Explained Like You’re Hearing It for the First Time — Self-Attention creates contextualized word embeddings by learning relationships between all words in a sequence.
• Build an LLM from Scratch 3: Coding attention mechanisms — Self-attention mechanisms enable LLMs to selectively process input sequences, crucial for generating contextually relevant outputs.
• The Transformer: A Beginner’s Deep Dive Into the Architecture That Changed AI Forever — The Transformer architecture replaces sequential processing with parallel self-attention, enabling context-aware, scalable, and efficient language understanding.
• Building LLM From Scratch: Understanding How Large Language Models Work — Large Language Models fundamentally predict the next token by processing numerical representations of text through attention mechanisms.
• Memory Sparse Attention to Get to 100 Million Tokens in Context — Self-distillation can harm reasoning by suppressing uncertainty, while sparse attention enables extreme context scaling.
• Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps — Full-attention LLMs are intrinsically sparse and can be efficiently transformed into sparse models with minimal training.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.