Building a Transformer LLM from Scratch using Low-Level PyTorch

The complexity of modern Large Language Models (LLMs) can feel like a black box, making it difficult to truly grasp how they function. This course strips away the high-level frameworks to reveal the fundamental mechanisms powering models like GPT. By the end of this course, you will have implemented the entire core Transformer architecture from scratch, gaining a deep, practical understanding of every layer, from raw text input to generated output. This hands-on, low-level approach ensures you gain the architectural knowledge required to debug, optimize, and innovate future models. What you'll learn: * Understand the mathematical foundations of the self-attention mechanism, multi-head attention, and positional encoding. * Implement Byte Pair Encoding (BPE) for efficient text tokenization and vocabulary management from raw data. * Build the full Decoder-only Transformer stack (like GPT) using only low-level PyTorch primitives and modules. * Practice modern Python and PyTorch conventions, including effective device management and robust implementation using static type hinting. * Apply techniques for text generation, including sampling and greedy decoding, to perform inference with your custom model. * Configure basic training loops and understand the crucial gradient flow necessary for optimizing large language models. The course begins with foundational concepts of sequence modeling and attention, then systematically guides you through implementing each component of the Transformer layer-by-layer in Python and PyTorch. You will connect these parts to form a functional, trainable LLM architecture ready for experimentation. This course is designed for beginner and intermediate developers familiar with basic Python syntax who want to transition into deep learning and AI engineering. No prior experience with PyTorch or neural network architectures is required. Start building your foundational knowledge in generative AI today.