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

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Understanding how transformers work in large language models (LLMs) can be achieved quickly by breaking down the steps involved in the process. Starting from tokenization, where input data is converted into tokens, these tokens are then embedded into numerical representations understood by the model. These embeddings are processed through multiple transformer layers that use attention mechanisms to determine the importance of each token in relation to others. Finally, the processed data is projected back onto the vocabulary to predict the next token in a sequence. This foundational knowledge helps in exploring further intricacies of models like GPT-2.

#ai

#machine-learning

#deep-learning

#transformers

#gpt

Feb 26, 2025•14m read time•From blog.det.life

Table of contents

10 minutes are all you need to understand how Transformers work in LLM Introduction How GPT process data and generate next token Step 1: Tokenization Step 2: Embedding Layers Step 3: Transformer Layers Step 4: Projecting to the Vocabulary for Token Prediction Further Reading and References