# [How Transformer LLMs Work](https://www.deeplearning.ai/short-courses/how-transformer-llms-work/?utm_campaign=handsonllm-launch&utm_medium=partner)

In this course, we take the content from the book and enhance it through a short course on the main components of what makes a Transformer LLM. This **highly animated** course, will further enhance the intuition you get from the book's content.
 
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    <a href="https://www.deeplearning.ai/short-courses/how-transformer-llms-work/?utm_campaign=handsonllm-launch&utm_medium=partner"><img src="../images/dlai.png" width="50%" height="50%"></a>
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## What you'll learn

* Gain an understanding of the key components of transformers, including tokenization, embeddings, self-attention, and transformer blocks, to build a strong technical foundation.
* Understand recent transformer improvements to the attention mechanism such as KV cache, multi-query attention, grouped query attention, and sparse attention.
* Compare tokenization strategies used in modern LLMS and explore transformers in the Hugging Face Transformers library.

