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/
...nanoGPT targets reproducing GPT-2 (124M params) and covers a lot of ground. This project strips it down to the essentials and scales it to a ~10M param model that trains on a laptop in under an hour...
Coincidentally, I just started on Build a Large Language Model (From Scratch), a repo/book/course by Sebastian Raschka [0][1][2]. Maybe it is a good problem to have to have to decide which learning resource to use.
I did it back in the day when fast.ai was relatively new with ULMFiT. This must have been when Bert was sota. The architecture allows you to train a base and specialize with a head. I used the entire Wikipedia for the base and then some GBs of tweets I had collected through the firehouse. I had access to a lab with 20 game dev computers. Must have been roughly GTX 2080s. One training cycle took about half a day for the tokenized Wikipedia so I hyper parameter tuned by running one different setting on each computer and then moving on with the winner as the starting point for the next day. It was always fun to come to work the next morning and check the results.
The engineering was horrible and very ad-hoc but I learned a lot. Results were ok-ish (I classified tweets) but it gave me a good perspective on the sheer GPU power (and engineering challenges) one would need to do this seriously. I didn't fully grasp the potential of generating output but spent quite some time chuckling at generated tweets (was just curious to try it).
I know it's a bit of a joke, but "I Built a Neural Network from Scratch in SCRATCH" gave me, a complete outsider, a lot of insight into how neural networks work.
I would start with linear algebra, some calculus and statistics and understand how a neural network - which really is just one type of ML - works, the learn the basics of CNN and RNN, then learn transformers and LLM.
But that is just me. I think is more useful to understand the how and whys before training a LLM.
I'm not sure using pytorch counts as "from scratch" anymore. I'm not saying you should avoid the stdlib or anything crazy, but at the point where you're pulling in for-purpose libraries it really doesn't seem like "from scratch" to me.
Can anyone suggest or come up with viable "use cases" of a custom LLM like this? I wouldn't mind giving it a try but ideally I'm looking for something that is not just a toy.
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[ 2.6 ms ] story [ 61.1 ms ] threadI doubt you have a machine big enough to make it "Large".
...nanoGPT targets reproducing GPT-2 (124M params) and covers a lot of ground. This project strips it down to the essentials and scales it to a ~10M param model that trains on a laptop in under an hour...
[0] https://github.com/rasbt/LLMs-from-scratch
[1] https://www.manning.com/books/build-a-large-language-model-f...
[2] https://magazine.sebastianraschka.com/p/coding-llms-from-the...
The engineering was horrible and very ad-hoc but I learned a lot. Results were ok-ish (I classified tweets) but it gave me a good perspective on the sheer GPU power (and engineering challenges) one would need to do this seriously. I didn't fully grasp the potential of generating output but spent quite some time chuckling at generated tweets (was just curious to try it).
https://www.youtube.com/watch?v=5COUxxTRcL0
But that is just me. I think is more useful to understand the how and whys before training a LLM.
A series of Jupyter notebooks explaining the whole machine learning mechanism, from the beginning
https://github.com/nickyreinert/DeepLearning-with-PyTorch-fr...
and of course also how to build an llm from scratch
https://github.com/nickyreinert/basic-llm-with-pytorch/blob/...