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  • Very weird coding style, did you run astyle --style=python on C code?

    Also, your LLM left a comment in the cuda source that it is untested, does the cuda stuff work?

    by Chu4eeno
  • yes yes tested on a 4070 ti 16gb everything worked without problems!
    by vforno
  • > Very weird coding style, did you run astyle --style=python on C code?

    I'm sure you mean it in a more curious way but this type of comment on a Show HN often comes across as too harshy/snarky/dismissive for what we want here (see https://news.ycombinator.com/showhn.html).

    by dang
  • Not sure, but the code is quite dense and lacking in comments. `nanoeuler` & `nanoeuler_check` is itself the binary checked straight into git with the `.log` file? All of the commit messages are "Add files via upload" and happened in quick succession.

    I suspect this is LLM generated, which is cool, but shouldn't then have the claim "forward and backward passes are written and verified by hand" unless it is true.

    Regarding the data, old texts from Gutenberg probably lowers the performance - especially as many texts are on purpose whimsical. Shakespeare for example made up words to be theatrical. You have a mix of different old English styles in the corpus - it's a terrible way to learn modern English. I had some success using .ZIM data archives from Kiwix as a source, you should get a more stable output using that data.

    by bArray
  • this is super interesting. Looking forward to trying this out!
    by ali_chherawalla
  • Really thanks If you need any help or have any questions I'm here.
    by vforno
  • How long was it trained for? How many tokens?
    by ericb
  • Hi, a couple of hours, not too much! Including sft!
    by vforno
  • Amazing, i’ll try to learn from your code. Thanks!
    by memezed
  • Thanks to you! I will update the model later to make it more and more optimized but you will see everything you need in the readme.
    by vforno
  • I'm genuinely curious how much of this is LLM generated?
    by isatty
  • Most part of trasformer and sft!
    by vforno
  • Mentioning neural ODE doesn't make sense here, as this is unrelated. Basically any implementation of transformer uses residuals, but you're not really training a neural ODE here.

    Also consider getting rid of the em-dashes. I don't know if you mostly vibe-coded this or not, but the README is pretty clearly AI generated.

    by tdesilva
  • Hi, thanks for the comment. Nanoeuler is starting as a study and research project that will obviously improve over time. I'll do my best to make the readme and other things more readable. Thank you very much.
    by vforno
  • Wow that's really cool i'll definitely check it out! have played around with machine learning algorithms built from scratch in c / cuda too, but once i hit the cuda part of it i kinda just left it to the side. i'm curious how did you use CUDA to optimize the matrix multiplications? how optimized is training, does it take much longer then using pytorch?
    by AndReics
  • Hi, in nanoeuler I use cuBLAS (NVIDIA's super-optimized library) for all matrix multiplications, with the tensor cores in TF32 mode. It's the same thing PyTorch uses underneath, so it's very fast. What I've optimized (and will improve even more) and written by hand are the kernels for the other parts (like FlashAttention, which gave a nice 3x speedup), while I've delegated the large matrices to cuBLAS. Training the 116M model on a 4070 runs well and in reasonable times. Compared to PyTorch, it's a bit slower (probably 1.5-2.5x), but nothing dramatic, especially considering it's all done from scratch without a framework and there are no other optimizations that would make it faster. I'm working on it.
    by vforno