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The third chapter teaches how to code attention mechanisms. The LLM breakthrough. We start with a simple version with non-trainable weights and make adjustments until we have multi-headed attention as used in GPT-2.

The second chapter covers embedding and tokenisation.

Some notes on reading the first chapter of the book 'Building a Large Language Model From Scratch' by Sebastian Raschka

I wanted to run a docker image of a little app I made on my Synolgy NAS without the extra effort of publishing the image to a registry. Here's how I did it.

Here's how I split a off multiple projects from a large solution into a new git repository and kept the history.