Building LLMs like ChatGPT from Scratch and Cloud Deployment

About this course

Large Language Models like GPT-4, Llama, and Mistral are no longer science fiction; they are the new frontier of technology, powering everything from advanced chatbots to revolutionary scientific discovery. But to most, they remain a "black box.

" While many can use an API, very few possess the rare and valuable skill of understanding how these incredible models work from the inside out.

What if you could peel back the curtain? What if you could build a powerful, modern Large Language Model, not just by tweaking a few lines of code, but by writing it from the ground up, line by line?

This course is not another high-level overview. It's a deep, hands-on engineering journey to code a complete LLM—specifically, the highly efficient and powerful Mistral 7B architecture—from scratch in PyTorch. We bridge the gap between abstract theory and practical, production-grade code. You won't just learn what Grouped-Query Attention is; you'll implement it. You won't just read about the KV Cache; you'll build it to accelerate your model's inference.

We believe the best way to achieve true mastery is by building. Starting with the foundational concepts that led to the transformer revolution, we will guide you step-by-step through every critical component. Finally, you'll take your custom-built model and learn to deploy it for real-world use with the industry-standard, high-performance vLLM Inference Engine on Runpod.

After completing this course, you will have moved from an LLM user to an LLM architect. You will possess the first-principles knowledge that separates the experts from the crowd and empowers you to build, debug, and innovate at the cutting edge of AI.You will learn to build and understand:

• The Origins of LLMs: The evolution from RNNs to the Attention mechanism that started it all.

The Transformer

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