10 minutes are all you need to understand how Transformers work in LLM

Large language models like GPT process text through tokenization, embedding, and transformer layers. Tokens are converted to numerical representations, which pass through multiple neural network layers to predict the next word. The process involves attention mechanisms, where tokens attend to each other, and softmax functions to calculate probabilities. After processing, the model projects the output to vocabulary size, selecting the most likely next token. This cycle repeats to generate text one token at a time.