Using PyTorch is not "LLMs from the ground up".
It's a fine PyTorch tutorial but let's not pretend it's something low level.
It's a fine PyTorch tutorial but let's not pretend it's something low level.
Like driving a car, you don't need to understand what's under the hood you start driving, but eventually understanding it makes you a better driver.
But it doesn’t start there. It uses top-down pedagogy, instead of bottom up.
Never had a piano, not even a fortepiano .. though reportedly he played one once.
At least when i saw the "Building LLMs from the Ground Up" what i expected was someone to open vim, emacs or their favorite text editor and start writing some C code (or something around that level) to implement, well, everything from the "ground" (the operating system's user space which in most OSes is around the overall level of C) and "up".
To a statistician or a practitioner approaching machine learning from a mathematical perspective, the computational details are a distraction.
Yes, these models would not be possible without automatic differentiation and massively parallel computing. But there is a lot of rich detail to consider in building up the model from first mathematical principles, motivating design choices with prior art from natural language processing, various topics related to how input data is represented and loss is evaluated, data processing considerations, putting things into context of machine, learning more broadly, etc. You could fill half a book chapter with that kind of content (and people do), without ever talking about computational details beyond a passing mention.
In my personal opinion, fussing over manual memory management is far afield from anything useful unless you want to actually work on hardware or core library implementations like Pytorch. Nobody else in industry is doing that.
People are looking at the ground up for a clear picture of what the thing is actually doing, so masking the important part of what is actually happening, then calling it “ground up” is disingenuous.
If you are interested in how the model works conceptually, how training works, how it represents text semantically, etc., then I maintain that computational details are an irrelevant distraction, not an essential foundation.
How about another analogy? Is SICP not a good foundation for learning about language design because it uses Scheme and not assembly or C?
But if all is relative and depends on your PoV that implies that there isn't actually a problem here, right? :-P
I don't think there is anything wrong with "building up the model from first mathematical principles" as you wrote, it just wasn't what i personally had in mind with the "from the ground up" part.
And FWIW i'm not that stuck up on the "vim and C" aspect, i used those as an example that i expected most would understand and leave little room for misinterpretation in what you'd have to work with (i.e. very very little) and have to implement yourself (pretty much everything) - personally i'd consider it from "the ground up" even if it was in C#, D, Java, JavaScript or even Python, as long as the implementation was done in a way that didn't rely on 3rd party libraries so that whatever is implemented in, say, Java could also be implementable in C#, D, JavaScript or Python with just whatever is available out of the box in those languages or even C, if one doesn't mind writing the extra bookkeeping functionality themselves.
To someone who already knows excel, from scratch with excel sheets instead of python may work with them.
Although if your claim is then that most programmers do not care about being effective, that I would tend to agree with given the 64 gigs of ram my basic text editors need these days.
While I agree it's good to know how your collections work. "Efficient key-value store" may be enough to use it effectively 80% of the time for somebody dabbling in Python.
Sadly I've met enough people that call themselves programmers that didn't even have such a surface level understanding of it.
Calling that from scratch is like saying "Just go to the store and tell them what you want" in a series called: "How to make sausage from scratch".
When I want to know how to do X from scratch I am not interested in "how to get X the fastest way possible", to be frank I am not even interested in "How to get X in the way others typically get it", what I am interested in is learning how to do all the stuff that is normally hidden away in dependencies or frameworks myself — or, you know, from scratch. And considering the comments here I am not alone in that reading.
For example, building a web server from scratch - I’d probably assume the presence of a sockets library or at the very least networking card driver support. For logging and configuration I’d assume standard I/o support.
It probably comes down to what you think makes LLMs interesting as programs.
E.g. when a title says it shows you how to do a thing in vanilla javascript from scratch bringing in jquery in the first step makes that tile a lie. If you bring in a hefty dependency on step 1 and run three imported function the vanilla javascript part might be fine, but the from scratch starts to do some heavy lifting.
NVDIA value lies only in pytorch and cuda optimizations with respect with pure c implementation, so saying that you need go lower level than cuda or pytorch means simply reinventing Nvidia. Good luck with that
2. I didn't say CUDA wouldn't be ground up or low level (please re-read) (I say in another comment about a no-code guide with CUDA, but it's obviously a joke)
3. And finally, I think your comment comes out as holier than thou and finger pointing and making a huge deal out of a minor semantic observation.
Pytorch is low level enough to understand and interpret each and every passage. In pytorch, you can use builtin transformers, or code them yourself down to the "lowest" level in which there's still a theoretical meaning. So pytorch is just a tool and your comment was just pompous and empty.
It’s definitely out there and in productive use.
Which engines in particular? I never found especially flexible ones.
Unfortunately, your argument is a well known fallacy and carries no weight.
E.g.
1. Programming basics
2. How to manipulate text using programs (reading, writing, tokenization, counting words, randomization, case conversion, ...)
3. How to extract statistical properties from texts (ngrams, etc, ...)
4. How to generate crude text using markov chains
5. Improving on markov chains and thinking about/trying out different topologies
Etc.
Sure markov chains are not exactly LLMS, but they are a good starting point to byild a intuition how programs can extract statistical properties from text and generate new text based on that. Also it gives you a feeling how programes can work on text.
If you start directly with a framework there is some essential understanding missing.