Deep Dive into LLMs: Building and Training GPT Models from Scratch

Course Overview

This comprehensive course takes you on an educational journey to build, pretrain, and fine-tune a Large Language Model (LLM) completely from scratch. By writing code from the ground up, you will gain a deep, intuitive understanding of how modern generative AI models like GPT and LLaMA operate.

What You'll Learn

• Data Preparation: Tokenize raw text, implement Byte Pair Encoding (BPE), and construct high-performance PyTorch DataLoaders with sliding windows.
• Architecture Implementation: Code self-attention, causal attention, and multi-head attention mechanisms from scratch.
• GPT Model Construction: Assemble transformer blocks with layer normalization, GELU activation functions, shortcut connections, and feed-forward networks.
• Pretraining & Generation: Pretrain your model on unlabeled text, implement decoding strategies (temperature scaling and top-k sampling), and load pretrained weights.
• Fine-Tuning & Alignment: Adapt your model for classification tasks and perform supervised instruction fine-tuning to build custom conversational agents.

Target Audience

• Python Developers & AI Engineers who want to move beyond using APIs and understand the mechanics of LLMs.
• Data Scientists & Machine Learning Engineers looking to master PyTorch for deep learning architectures.
• Researchers seeking a clear, code-first walkthrough of transformer components.

Prerequisites

• Proficient Python Programming: Familiarity with object-oriented programming in Python.
• PyTorch Basics: Understanding of tensors and basic neural network concepts.
• Machine Learning Fundamentals: Conceptual knowledge of training loops, loss functions, and optimization.

Tools & Technologies Used

• Python (managed via the modern uv environment tool)
• PyTorch (for neural network construction and training)
• tiktoken (for Byte Pair Encoding tokenization)
• Jupyter Notebooks (for interactive experimentation)