Building an LLM from Scratch: The Foundational Layer and Structural Stability

2026-06-21 · Source: AI on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

The article details the foundational layers and structural stability required to build a Large Language Model (LLM) from scratch, drawing inspiration from nanoGPT architecture. It begins by explaining Token Embeddings, which map 65 unique characters from a Shakespeare dataset into 8-dimensional numerical vectors using PyTorch's "nn.Embedding". Next, Positional Embeddings are introduced to inject spatial awareness, combining with token embeddings via broadcasting. The text then covers PreNorm Layer Normalization, crucial for stabilizing data by normalizing mean and variance per token. The core Self-Attention mechanism is explained, including its Query, Key, and Value components, and how four problems (dimension scaling, normalization, causal masking, dropout) are addressed. This leads to Multi-Head Attention and the FeedForward Network for individual token computation. Finally, the Vocabulary Logits layer decodes processed vectors back into raw prediction scores for text generation.

Key takeaway

For AI Engineers building custom LLMs or exploring Transformer architectures, understanding these foundational components is critical. You should prioritize robust implementation of token and positional embeddings, layer normalization, and multi-head attention to ensure structural stability and contextual understanding. Focus on correctly applying causal masking and dimension scaling within attention mechanisms to prevent common pitfalls and enable effective model training and text generation.

Key insights

LLM foundational layers convert text to contextualized numerical representations via embeddings, attention, and normalization.

Principles

• Token embeddings map discrete units to dense vectors.
• Positional embeddings inject sequence order.
• Self-attention dynamically relates tokens for context.

Method

Construct an LLM by sequentially implementing token and positional embeddings, layer normalization, multi-head attention, and a feedforward network, culminating in a vocabulary logits layer for decoding.

In practice

• Use "nn.Embedding" for token and position embeddings.
• Implement "nn.LayerNorm" for data stabilization.
• Apply causal masking to prevent future token leakage.

Topics

• LLM Architecture
• Token Embeddings
• Positional Embeddings
• Self-Attention
• Multi-Head Attention
• Layer Normalization
• PyTorch

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

Related on AIssential

• How Transformers Architecture Powers Modern LLMs — LLMs use a transformer architecture to convert text into numerical representations, process context, and predict the next token.
• Building an LLM From Scratch: The Mechanism That Changed AI Forever, Implemented From Zero — Self-attention enables LLMs to understand word relevance by dynamically weighting contextual dependencies.
• 6 Things I Learned Building LLMs From Scratch That No Tutorial Teaches You — LLM architecture involves non-obvious design choices significantly impacting performance, cost, and training stability.
• Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs — Training an LLM from scratch involves understanding core components like tokenization, transformer architecture, and the training loop.
• Ful LLM / Deep Learning roadmap. — Transformers leverage attention mechanisms and positional encodings to process sequences bidirectionally or causally.
• Build an LLM from Scratch 2: Working with text data — Text data preparation for LLMs involves tokenization, converting tokens to IDs, and generating contextual embeddings.

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