Build, Align, and Fine-Tune LLMs from Scratch with PyTorch

Understanding how Large Language Models (LLMs) work under the hood is the key to mastering modern generative AI. This text-based guide demystifies deep learning by helping you construct, train, and align your own neural networks from the ground up. You will transition from an AI enthusiast to a developer who understands the exact mechanics of transformer architectures. Through clear written explanations and step-by-step PyTorch code analysis, you will explore how data flows through attention layers, how models are aligned for safety and utility, and how to efficiently fine-tune open-source models on standard hardware. What you'll learn: - Understand the core mathematical foundations of transformers, attention mechanisms, and high-dimensional space folding using intuitive paper-folding analogies. - Build a functional Large Language Model from scratch using Python and PyTorch, writing the layers and training loops line by line. - Apply parameter-efficient fine-tuning techniques like QLoRA to adapt existing open-source models to custom datasets efficiently. - Align models using modern training paradigms to ensure helpful, safe, and structured outputs. - Analyze attention matrices and weights conceptually to comprehend how deep learning models process and generate language. The journey begins with foundational deep learning concepts, translating complex mathematical abstractions into physical analogies like origami. From there, you will read through the step-by-step implementation of a transformer architecture, culminating in practical alignment and parameter-efficient fine-tuning workflows. This course is designed for aspiring AI engineers, data scientists, and developers with a basic understanding of Python who want a deep, conceptual, and code-level understanding of generative AI. No prior deep learning experience is required. Start reading today to unlock the inner workings of modern language models and build your own AI systems from scratch.