How Transformer LLMs Work

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.

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.