Pretraining Large Language Models from Scratch

Large language models are transforming technology, but understanding how they are built from the ground up remains a rare and valuable skill. This text-based course guides you through the foundational concepts and practical steps of pretraining your own language model. You will transition from a consumer of AI models to someone who understands the entire lifecycle of model creation, from raw text preparation to initial training runs. What you'll learn: 1. Learn the core mathematical and architectural concepts behind modern Transformer models. 2. Master data curation strategies, including filtering, deduplication, and quality control. 3. Configure custom tokenizers to convert raw text into model-ready numerical inputs. 4. Understand distributed training fundamentals and mixed-precision techniques to optimize performance. 5. Practice writing training loops and setting up loss monitoring using clear, step-by-step code examples. 6. Explore modern pretraining optimizations such as rotary position embeddings and RMSNorm. Starting with essential terminology and foundational definitions, this course guides you through the pipeline of data preparation, architectural setup, and computational execution. You will read detailed explanations and analyze code implementations designed to demystify the pretraining process. This course is designed for beginners and tech enthusiasts looking to understand LLM internals from scratch, with no prior machine learning experience required. Begin your journey into the core of modern artificial intelligence today.