Build and Train an LLM from Scratch

2026-01-02 · Source: AI Advances - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

This guide details the end-to-end process of building and training a text-generation Large Language Model (LLM) from scratch, leveraging previously established components like a custom Decoder-only Transformer and a tokenizer. The training process utilizes the WikiText-103-v1 dataset, a collection of approximately 103 million words derived from verified Wikipedia articles, accessed via Hugging Face's `datasets` library. The article demonstrates how to load the "train" subset of this dataset and provides an example of its textual content, illustrating the initial steps required to prepare data for LLM training. This builds upon foundational knowledge of Transformer architecture and tokenizer implementation.

Key takeaway

For AI Engineers building custom LLMs, understanding the complete training pipeline from data acquisition to model execution is crucial. You should prioritize modular development, starting with robust Transformer and tokenizer implementations before integrating them for end-to-end training. Utilizing established datasets like WikiText-103-v1 can streamline your data preparation phase, allowing you to focus on model architecture and training optimization.

Key insights

Training an LLM from scratch involves data preparation, custom Transformer architecture, and tokenizer implementation.

Principles

• WikiText-103-v1 is suitable for LLM training.
• Modular components simplify LLM development.

Method

The method involves loading the WikiText-103-v1 dataset using `datasets.load_dataset` for LLM training, after implementing a Decoder-only Transformer and a custom tokenizer.

In practice

• Use `load_dataset` for Hugging Face datasets.
• Inspect dataset examples for content.

Topics

• Large Language Models
• Decoder-only Transformers
• Text Generation
• WikiText Dataset
• LLM Training

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

Related on AIssential

• 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.
• 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.
• Developing an LLM: Building, Training, Finetuning — LLM development involves building architecture, pre-training on vast datasets, and fine-tuning for specific tasks and behaviors.
• IEEE Rolls Out Large Language Models Virtual Training Course — Large language models are fundamental architectural components demanding deep technical understanding for reliable and secure integration into digital infrastructures.
• FareedKhan-dev / train-llm-from-scratch — Building a Transformer LLM from scratch reveals practical challenges and opportunities for model scaling and domain-specific fine-tuning.
• 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.

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