So far, every token enters the model as an ID, becomes an embedding, receives position information, and passes through Transformer layers. At the end of that process, every token has a contextual vector. But a language model does not output text directly, it outputs probabilities. The final linear layer After the Transformer, we take the… Continue reading Building my own language model: Predicting the next token (Part 5)