The best way to really understand how something works is to build it yourself. So I am wondering if there are any good tutorials on building your own LLM from scratch. I.e. implementing tokenisation, embeddings, attention and so on. I am not suggesting one could replicate chatGPT, but more a toy model that implements the core features but based on a much smaller corpus and training data.
Generally, I think the Karpathy tutorials are a good starting point but they're very mathy (despite people telling you you only need high school math to understand llms, a lot of the abstractions and concepts he uses are a bit foreign to programmers).
I found out rebuilding inference of a known model taught me a lot more than passively sitting through the videos and maybe retyping his code. You should try it with something simple, like a model from a few years back!
Generally, I think the Karpathy tutorials are a good starting point but they're very mathy (despite people telling you you only need high school math to understand llms, a lot of the abstractions and concepts he uses are a bit foreign to programmers).
I found out rebuilding inference of a known model taught me a lot more than passively sitting through the videos and maybe retyping his code. You should try it with something simple, like a model from a few years back!
The shortcut is Karpathy's "Let's Build GPT: from scratch, in code, spelled out" video:
https://www.youtube.com/watch?v=kCc8FmEb1nY
Then there is a good video that dives into LLMs and how they work that is quite approachable:
https://www.youtube.com/watch?v=7xTGNNLPyMI
From there, flesh out knowledge with his other videos, where he goes both extremely light and extremely deep:
https://www.youtube.com/@AndrejKarpathy/videos
Anyway, I really like's Karpathy's video because he's very good at explaining LLMs at every level.
https://github.com/karpathy/nanoGPT
https://mathstodon.xyz/@empty/115088095028020763