Coding LLMs from the Ground Up
- Source
- magazine.sebastianraschka.com
- Author
- Sebastian Raschka, PhD
- Date
Context
Sebastian Raschka's video-course description presents building a small as a way to understand the stages behind a finished AI system. The sequence moves from preparing text and implementing to , then adapting the model for specific tasks.
The progression makes a useful distinction visible: building the architecture, learning from unlabeled text and teaching task behavior are separate steps. The described spam-classification exercise introduces adaptation before the course turns to instruction following. Its educational purpose is to make those steps inspectable through code, so learners can connect a model's behavior to how it was built.
- Raschka's case for building from scratch is the go-kart argument: you don't learn engines by assembling a Formula 1 car. A tiny model still teaches steering and motor, at a cost and complexity you can actually finish.
- The sequence puts mechanism before architecture: text prep and byte-pair tokenization first, then self, causal, and multi-head attention coded by hand over 2h15, then the model shell, pretraining, classifier , and instruction tuning.
- Windows installs break on the TensorFlow dependency used to load OpenAI's original GPT-2 weights. Delete that line from the requirements file and pull Raschka's PyTorch-converted GPT-2 weights from Hugging Face instead.
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
- attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
- pretraining — The first, biggest phase of building a model: training it on enormous amounts of text so it learns language, facts, and reasoning in general.
- fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
“It's probably the best and most efficient way to learn how LLMs really work.”
“After all, the best race drivers often got their start by building and tinkering with their own go-karts (think Michael Schumacher and Ayrton Senna).”
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