Hacker News Reader

Question for the author:

I'm not interested in language models specifically, but there are techniques involved with language models I would like to understand better and use elsewhere. For example, I know "attention" is used in a variety of models, and I know transformers are used in more than just language models. Will this book help me understand attention and transformers well enough that I can use them outside of language models?

View on HN

The attention mechanism we implement in this book* is specific to LLMs in terms of the text inputs, but it's fundamentally the same attention mechanism that is used in vision transformers. The only difference is that in LLMs, you turn text into tokens, and convert these tokens into vector embeddings that go into an LLM. In vision transformers, instead of regarding images as tokens, you use an image patch as a token and turn those into vector embeddings (a bit hard to explain without visuals here). In both text or vision context, it's the same attention mechanism, and it both cases it receives vector embeddings.

(*Chapter 3, already submitted last week and should be online in the MEAP soon, in the meantime the code along with the notes is also available here: https://github.com/rasbt/LLMs-from-scratch/blob/main/ch03/01...)

Reply on news.ycombinator.com