A hands-on route into language models
A resource for understanding the machinery behind the API.
A resource to build alongside
Sebastian Raschka’s book and companion materials work through text representations, attention, GPT-style model components, pretraining, and fine-tuning using Python and PyTorch.
I shared it as a hands-on learning resource. It offers a concrete route from concepts to runnable implementations instead of stopping at model API calls.
A practical way to use it
Read a section, run its implementation, and then change one thing. Inspect tensor shapes and compare the result with what you expected. The exercises provide a useful check on understanding.
A small implementation that you can inspect is a useful starting point before thinking about large-scale training.
Curated by Kishan Vyas on . The date at the top preserves my original share date.