If you're interested in this resource, I highly recommend checking out Stanford's CS336 class. It covers all this curriculum in a lot more depth, introduces you into a lot of theoretical aspects (scaling laws, intuitions) and systems thinking (kernel optimization/profiling). For this, you have to do the assignments, of course... https://cs336.stanford.edu/
Hey now! I've got a half terabyte of RAM at my disposal! I mean, it's DDR4 but... it's RAM!
And it's paired with 48 processor cores! I mean, they don't even support AVX512 but they can do math!
I could totally train a LLM! Or at least my family could... might need my kid to pick up and carry on the project.
But in all seriousness... you either missed the point, are being needlessly pedantic, or are... wrong?
This is about learning concepts, and the rest of this is mostly moot.
On the pedantic or wrong notes--What is the documented cut-off for a "large" language model? Because GPT-2 was and is described as a "large" language model. It had 1.5B parameters. You can just about get a consumer GPU capable of training that for about $400 these days.
If you're interested in this resource, I highly recommend checking out Stanford's CS336 class. It covers all this curriculum in a lot more depth, introduces you into a lot of theoretical aspects (scaling laws, intuitions) and systems thinking (kernel optimization/profiling). For this, you have to do the assignments, of course... https://cs336.stanford.edu/
how does one get the lectures? I don't see the option for any lectures.
https://github.com/stanford-cs336/lectures
Nice. What scale does this realistically reach on a single machine?
Train your LM from scratch*
I doubt you have a machine big enough to make it "Large".
Hey now! I've got a half terabyte of RAM at my disposal! I mean, it's DDR4 but... it's RAM!
And it's paired with 48 processor cores! I mean, they don't even support AVX512 but they can do math!
I could totally train a LLM! Or at least my family could... might need my kid to pick up and carry on the project.
But in all seriousness... you either missed the point, are being needlessly pedantic, or are... wrong?
This is about learning concepts, and the rest of this is mostly moot.
On the pedantic or wrong notes--What is the documented cut-off for a "large" language model? Because GPT-2 was and is described as a "large" language model. It had 1.5B parameters. You can just about get a consumer GPU capable of training that for about $400 these days.
This looks great for a first introduction to training LLMs, and it looks simple enough to try this locally. Great job!