Tutorial lets you stack transformer blocks like legos to build gpt—because nothing screams 'easy ai pivot' like a pytorch notebook that ignores the trillion-dollar training bill

In the fourth session of the "Fazendo um LLM do Zero" series, the focus shifts from collecting basic materials to building the architecture of a Large Language Model (LLM). As detailed in Sebastian Raschka's book "Build a Large Language Model (From Scratch)", the key to the intelligence of these models lies in the repetitive and organized stacking of simple blocks. The fundamental block is the Transformer Module, which contains attention and feedforward networks. The data flow is rigorously ordered, with information passing through a production line-like process within each block. The feedforward network processes individual words to extract deeper meanings, while residual connections and layer normalization ensure the model's stability. With the implementation of the TransformerBlock, LayerNorm, and residual connections, the GPTMini model is now ready to be trained, having all the physical structure necessary to learn any language.