Large Language Model Pretraining: Foundations of Scaling and Data Curation

Behind every powerful AI assistant lies a base language model pretrained on trillions of words. Understanding how these massive models are built from the ground up is essential for anyone looking to navigate the modern AI landscape. This text-only course demystifies the complex world of pretraining at scale, guiding you through the exact methodologies used to create foundational AI models. You will progress from core architectural concepts to the practicalities of data processing, compute optimization, and scaling dynamics, gaining a clear conceptual map of the entire pretraining pipeline. What you'll learn: - Understand the foundational architecture of modern transformer-based language models. - Analyze dataset curation strategies, including deduplication, quality filtering, and synthetic data generation. - Explore tokenization algorithms and how text is converted into numerical representations. - Apply scaling laws to predict model performance based on compute, parameters, and dataset size. - Examine the infrastructure challenges of distributed training across large GPU clusters. - Discover how emergent abilities arise in base models during the pretraining phase. We begin by establishing core definitions and the mathematical foundations of language modeling. From there, the material guides you through data pipelines, tokenizer design, compute-optimal training configurations, and the mechanics of training models at scale. This course is designed for curious beginners, software engineers, and technical product managers who want to understand how base models are built without needing an advanced degree in machine learning. No prior experience with supercomputing is required. Start reading today to build a strong conceptual foundation in the engineering of large language models.