LLMs-from-scratch VS ml-engineering

https://www.manning.com/books/build-a-large-language-model-f...

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...)

There is a lot of work to make the actual infrastructure and lower level management of lots and lots of GPUs/TPUs open as well - my team focuses on making the infrastructure bit at least a bit more approachable on GKE and Kubernetes.

https://github.com/GoogleCloudPlatform/ai-on-gke/tree/main

and

https://github.com/google/xpk (a bit more focused on HPC, but includes AI)

and

https://github.com/stas00/ml-engineering (not associated with GKE, but describes training with SLURM)

The actual training is still a bit of a small pool of very experienced people, but it's getting better. And every day serving models gets that much faster - you can often simply draft on Triton and TensorRT-LLM or vLLM and see significant wins month to month.

OK, the pdf is ready now: https://github.com/stas00/ml-engineering#pdf-version

this might be interesting: https://github.com/stas00/ml-engineering/blob/master/transformers/make-tiny-models.md