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This course provides an in-depth, hands-on journey through the complete implementation of a GPT-style language model, similar to OpenAI’s GPT-2. Built entirely using PyTorch, this codebase shows you how to tokenize data, construct Transformer-based models (including causal self-attention and MLP blocks), train efficiently with distributed training (DDP + gradient accumulation), evaluate with loss and accuracy metrics (including HellaSwag tasks), and generate text in an autoregressive fashion.You will not just use Hugging Face tools—you will replicate how GPT works at the core. This means building positional embeddings, attention heads, model layers, training loops, learning rate schedulers, validation steps, and generation logic—all from scratch.Whether you're an AI researcher, developer, or enthusiast, this course will give you an insider's view of what powers ChatGPT and how you can create your own scaled-down version for specific domains or experiments.