Sébastien

I finally understand how GPT generates text. Really helps to code it from scratch in Python. There are 5 components: • token embeddings • positional embeddings • transformer blocks • layer normalization • output head It sounds complex, but grokking GPT is simple. Token embeddings turn input text into meaningful vectors that capture semantic meaning. Positional embeddings encode positions of input tokens. This tells GPT "where" each token is in the input text. Transformer blocks are the processing powerhouse. This is where attention and training happens. Layer normalization smooths out the data, which enhances training stability. Output head translates the learned features into a next token prediction. And that's it! I recommend running the code below and then implementing it step-by-step on your own. Happy learning.