Building LLMs from Scratch

I am fascinated by the inner workings of Large Language Models but diving (finding the right ones too) into research papers often feels like trying to decode ancient hieroglyphics. That's why this book by Sebastian Raschka, PhD caught my attention with its promise of building LLMs from scratch. Spoiler alert: it actually delivers on that promise, and here's why this matters.

The Evolution of Understanding

The journey starts gently with encoders and decoders, but what grabbed me was the historical narrative tucked into Chapter 3. Did you know that before the transformer architecture took over our field, there was this pivotal moment with something called the Bahdanau attention mechanism? It's like discovering that your favorite band was inspired by an obscure artist you've never heard of. This mechanism for RNNs laid the groundwork for the self-attention we all know and accept as a given today.

Building Blocks and Aha Moments

What sets this book apart is its "show-don't-tell" approach. Instead of drowning you in theory, each chapter builds another piece of the LLM puzzle. Here's what fascinated me:

The tokenization chapter doesn't just explain byte-pair encoding – it shows you why we need it by building a simple tokenizer and watching it fail in interesting ways. It's like watching a time-lapse of engineering evolution.

Then comes the "heady" ride through attention mechanisms. The author does something clever here: introduces complex concepts like scaled-dot product attention by connecting them to familiar mathematical concepts. When explaining dot products, they tie it back to basic linear algebra in a way that made me go "oh, so THAT'S why we use them!"

The Magic Moment: When Theory Meets Practice

By Chapter 4, something magical happens. The model starts generating text – terrible text at first, but text nonetheless. It's like watching a baby take its first steps. The author periodically shows these outputs throughout the book, and watching them evolve from gibberish to coherent text is surprisingly emotional.

More comes in Chapter 5. Instead of just throwing terms like "cross-entropy" and "perplexity" at you, the author breaks them down into digestible pieces. They explain logits, probabilities, and loss calculations in a way that feels like having coffee with a really smart friend who's excited to share their knowledge.

The Democratic Nature of AI

Here's the most exciting thing: everything in this book runs on a regular laptop. No fancy GPUs required. In an era where we often associate AI with massive compute requirements, this is refreshingly accessible. I ran all the code on a modest machine, and it worked beautifully.

Beyond the Basics

The later chapters on fine-tuning and instruction following feel like bonus content, but they're equally well-crafted. The addition of practical tools like ollama for evaluation and the coverage of LoRA in the appendices shows the author's commitment to keeping things current and practical.

The Perfect Companion to Modern AI Education

If you're like me, you've probably spent countless hours watching Andrej Karpathy's legendary youtube videos or diving deep into his blogs about neural networks and transformers. What makes this book particularly valuable is how it complements and expands upon that foundation. While Karpathy's work gives you the bird's eye view and intuition of neural networks, this book feels like the patient mentor who sits beside you and walks through every nuanced detail.

What's fascinating is how the book takes concepts that Karpathy introduces in his videos – like attention mechanisms and transformer architectures – and provides that crucial next layer of understanding. For instance, where Karpathy might spend 15 minutes explaining the intuition behind attention, this book dedicates an entire chapter to building it from scratch, helping you understand not just the what, but the precise how and why of each component.

Why This Book Matters

We're living in an extraordinary time where practitioners at the forefront of AI are willing to share their knowledge in such an accessible way. Between Karpathy's pioneering educational content and books like this that provide deep, hands-on implementation details, there's never been a better time to learn about LLMs. This book breaks down what could have been impenetrable concepts into what I like to call "bite-sized morsels of understanding" (pun intended for those who've read it).

Final Thoughts

Whether you're a student, a practicing engineer, or just someone curious about how these models actually work, you'll find value here.

The author maintains an active GitHub repository with all the code, and the community around it is growing. It's like getting a backstage pass to the LLM revolution.

If you've read this far, I'd love to hear your thoughts. What resources have you found helpful in understanding LLMs? Has anyone else worked through this book? Let's discuss in the comments! ????

#MachineLearning #AI #LLM #TechnicalReading #AIEngineering