nn-zero-to-hero VS nanoGPT

Andrej covers this in https://github.com/karpathy/nn-zero-to-hero. He explains things in multiple ways, both the matrix multiplications as well as the "programmer's" way of thinking of it - i.e. the lookups. The downside is it takes a while to get through those lectures. I would say for each 1 hour you need another 10 to looks stuff up and practice, unless you are fresh out of calculus and linear algebra classes.

All the needed course material is here: https://github.com/karpathy/nn-zero-to-hero

That concept is not the easiest to describe succinctly inside a file like this, I think (especially as there are various levels of 'beginner' to take into account here). This is considered a very entry level concept, and I think there might be others who would consider it to be noise if logged in the code or described in the comments/blogpost.

After all, there was a disclaimer that you might have missed up front in the blogpost! "This post assumes familiarity with Python, NumPy, and some basic experience training neural networks." So it is in there! But in all of the firehose of info we get maybe it is not that hard to miss.

However, I'm here to help! Thankfully the concept is not too terribly difficult, I believe.

Effectively, the loss function compresses the task we've described with our labels from our training dataset into our neural network. This includes (ideally, at least), 'all' the information the neural network needs to perform that task well, according to the data we have, at least. If you'd like to know more about the specifics of this, I'd refer you to the original Shannon-Weaver paper on information theory -- Weaver's introduction to the topic is in plain English and accessible to (I believe) nearly anyone off of the street with enough time and energy to think through and parse some of the concepts. Very good stuff! An initial read-through should take no more than half an hour to an hour or so, and should change the way you think about the world if you've not been introduced to the topic before. You can read a scan of the book at a university hosted link here: https://raley.english.ucsb.edu/wp-content/Engl800/Shannon-We...

Using some of the concepts of Shannon's theory, we can see that anything that minimizes an information-theoretic loss function should indeed learn as well those prerequisites to the task at hand (features that identify xyz, features that move information about xyz from place A to B in the neural network, etc). In this case, even though it appears we do not have labels -- we certainly do! We are training on predicting the _next words_ in a sequence, and so thus by consequence humans have already created a very, _very_ richly labeled dataset for free! In this way, getting the data is much easier and the bar to entry for high performance for a neural network is very low -- especially if we want to pivot and 'fine-tune' to other tasks. This is because...to learn the task of predicting the next word, we have to learn tons of other sub-tasks inside of the neural network which overlap with the tasks that we want to perform. And because of the nature of spoken/written language -- to truly perform incredibly well, sometimes we have to learn all of these alternative tasks well enough that little-to-no-finetuning on human-labeled data for this 'secondary' task (for example, question answering) is required! Very cool stuff.

This is a very rough introduction, I have not condensed it as much as it could be and certainly, some of the words are more than they should be. But it's an internet comment so this is probably the most I should put into it for now. I hope this helps set you forward a bit on your journey of neural network explanation! :D :D <3 <3 :)))))))))) :fireworks:

For reference, I'm interested very much in what I refer to as Kolmogorov-minimal explanations (Wikipedia 'Kolmogorov complexity' once you chew through some of that paper if you're interested! I am still very much a student of it, but it is a fun explanation). In fact (though this repo performs several functions), I made https://github.com/tysam-code/hlb-CIFAR10 as beginner-friendly as possible. One does have to make some decisions to keep verbosity down, and I assume a very basic understand of what's happening in neural networks here too.

I have yet to find a good go-to explanation of neural networks as a conceptual intro (I started with Hinton -- love the man but extremely mathematically technical for foundation! D:). Karpathy might have a really good one, I think I saw a zero-to-hero course from him a little while back that seemed really good.

Andrej (practically) got me into deep learning via some of his earlier work, and I really love basically everything that I've seen the man put out. I skimmed the first video of his from this series and it seems pretty darn good, I trust his content. You should take a look! (Github and first video: https://github.com/karpathy/nn-zero-to-hero, https://youtu.be/VMj-3S1tku0)

For reference, he is the person that's made a lot of cool things recently, including his own minimal GPT (https://github.com/karpathy/minGPT), and the much smaller version of it (https://github.com/karpathy/nanoGPT). But of course, since we are in this blog post I would refer you to this 60 line numpy GPT first (A. to keep us on track, B. because I skimmed it and it seemed very helpful! I'd recommend taking a look at outside sources if you're feeling particularly voracious in expanding your knowledge here.)

I hope this helps give you a solid introduction to the basics of this concept, and/or for anyone else reading this, feel free to let me know if you have any technically (or-otherwise) appropriate questions here, many thanks and much love! <3 <3 <3 <3 :DDDDDDDD :)))))))) :)))) :))))

6️⃣ karpathy/nn-zero-to-hero

I’m a software engineer with zero experience with ml but have an interest in learning. I am confortable programming in any dynamic object oriented language. My basic plan to get started is to spend some time with the mathematical foundations of ml (Udemy course Mathematical foundations of Machine learning on Udemy looks decent). It also covers these concepts in the context of popular ml frameworks such as tensorflow and PyTorch so that’s kind of a two for one. I also stumbled upon this course: https://github.com/karpathy/nn-zero-to-hero.

Nice work! I built something similar years ago and I did compile the probabilities based on a corpus of text (public domain books) in an attempt to produce writing in the style of various authors. The results were actually quite similar to the output of nanoGPT[0]. It was very unoptimized and everything was kept in memory. I also knew nothing about embeddings at the time and only a little about NLP techniques that would certainly have helped. Using a graph database would have probably been better than the datastructure I came up with at the time. You should look into stuff like Datalog, Tries[1], and N-Triples[2] for more inspiration.

You're idea of splitting the probabilities based on whether you're starting the sentence or finishing it is interesting but you might be able to benefit from an approach that creates a "window" of text you can use for lookup, using an LCS[3] algorithm could do that. There's probably a lot of optimization you could do based on the probabilities of different sequences, I think this was the fundamental thing I was exploring in my project.

Seeing this has inspired me further to consider working on that project again at some point.

[0] https://github.com/karpathy/nanoGPT

[1] https://en.wikipedia.org/wiki/Trie

[2] https://en.wikipedia.org/wiki/N-Triples

[3] https://en.wikipedia.org/wiki/Longest_common_subsequence

where x1 denotes the 1st token, x2 denotes the 2nd token and so on, respectively.

I understand the conditional terms p(x_n|...) where we use cross-entropy to calculate their losses. However, I'm unsure about the probability of the very first token p(x1). How is it calculated? Is it in some configurations of the training process, or in the model architecture, or in the loss function?

IMHO, if the model doesn't learn p(x1) properly, the entire formula for Bayes' rule cannot be completed, and we can't refer to LLMs as "truly generative". Am I missing something here?

I asked the same question on nanoGPT repo: https://github.com/karpathy/nanoGPT/issues/432, but I haven't found the answer I'm looking for yet. Could someone please enlighten me.

This repo, albeit "old" in regards to how much progress there's been in LLMs, has great simple tutorials right there eg. fine-tuning GPT2 with Shakespeare: https://github.com/karpathy/nanoGPT

For training from scratch, maybe a small model like https://github.com/karpathy/nanoGPT or tinyllama. Perhaps with quantization.

It does remind me of a project [1] Andrej Karpathy did, writing a neural network and training code in ~600 lines (although networks have easier logic to code than a compiler).

[1] https://github.com/karpathy/nanoGPT

But I'm still not convinced that it can't in theory. Maybe the training set or transformer size I'm using is too small. I'm using nanoGPT implementation (https://github.com/karpathy/nanoGPT) with layers 24, heads 12, and embeddings per head 32. I'm using character-based vocab: every digit is a separate token, +, = and EOL.

https://github.com/karpathy/nanoGPT/blob/f08abb45bd2285627d1...

At training time, probabilities for the next token are computed for each position, so if we feed in a sequence of n tokens, we basically get n training examples, one for each position, but at inference time, we only compute the next token since we’ve already output the preceding ones.

And there are a bunch of other efforts at making training more efficient. Here's a cool model by Karpathy (OpenAI/used to head up Tesla's efforts): https://github.com/karpathy/nanoGPT

Just being a part of any auto-regressive system does not contradict his statement.

Go look at the GPT training code, here is the exact line: https://github.com/karpathy/nanoGPT/blob/master/train.py#L12...

The model is only trained to predict the next token. The training regime is purely next-token prediction. There is no loopiness whatsoever here, strange or ordinary.

Just because you take that feedforward neural network and wrap it in a loop to feed it its own output does not change the architecture of the neural net itself. The neural network was trained in one direction and runs in one direction. Hofstadter is surprised that such an architecture yields something that looks like intelligence.

He specifically used the correct term "feedforward" to constrast with recurrent neural networks, which GPT is not: https://en.wikipedia.org/wiki/Feedforward_neural_network

Does anyone have or know of an example implementation in plain pytorch, not huggingface transformers. Like something you could plug into https://github.com/karpathy/nanoGPT ?