Cool stuff! I can see some GPT comments that can be removed
// Increased for better learning
this doesn't tell me anything
// Use the constants from lib.rs
const MAX_SEQ_LEN: usize = 80;
const EMBEDDING_DIM: usize = 128;
const HIDDEN_DIM: usize = 256;
these are already defined in lib.rs, why not use them (as the comment suggests)
untrimmed · 1h ago
As someone who has spent days wrestling with Python dependency hell just to get a model running, a simple cargo run feels like a dream. But I'm wondering, what was the most painful part of NOT having a framework? I'm betting my coffee money it was debugging the backpropagation logic.
ricardobeat · 13m ago
Have you tried uv [1]? It has removed 90% of the pain of running python projects for me.
lowkey ppl who praise cargo seem to have no idea of the tradeoffs involved in dependency management
the difficulty of including a dependency should be proportional to the risk you're taking on, meaning it shouldn't be as difficult as it in, say, C where every other library is continually reinventing the same 5 utilities, but also not as easy as it is with npm or cargo, because you get insane dependency clutter, and all the related issues like security, build times, etc
how good a build system isn't equivalent of how easy it is include a dependency, while modern languages should have a consistent build system, but having a centralised package repository that anyone freely pull to/from, and having those dependencies freely take on any number of other dependencies is a bad way to handle dependencies
quantumspandex · 15m ago
Security is another problem, and should be tackled systematically. Artificially making dependency inclusion hard is not it and is detrimental to the more casual use cases.
jokethrowaway · 1m ago
Is your argument that python's package management & ecosystem is bad by design - to increase security?
In my experience it's just bugs and poor decision making on the maintainers (eg. pytorch dropping support for intel mac, leftpad in node) or on the language and package manager developers side (py2->3, commonjs, esm, go not having a package manager, etc).
Cargo has less friction than pypi and npm. npm has less friction than pypi.
And yet, you just need to compromise one lone, unpaid maintainer to wreck the security of the ecosystem.
itsibitzi · 16m ago
What tool or ecosystem does this well, in your opinion?
Snuggly73 · 11m ago
Congrats - there is a very small problem with the LLM - its reusing transformer blocks and you want to use different instances of them.
Its a very cool excercise, I did the same with Zig and MLX a while back, so I can get a nice foundation, but since then as I got hooked and kept adding stuff to it, switched to Pytorch/Transformers.
icemanx · 8m ago
correction: It's a cool exercise if you write it yourself and not use GPT
Snuggly73 · 4m ago
well, hopefully the author did learn something or at least enjoyed the process :)
(the code looks like a very junior or a non-dev wrote it tbh).
linking both rand-core 0.9.0 and rand-core 0.9.3 which the project could maybe avoid by just specifying 0.9 for its own dep on it
tonyhart7 · 1h ago
is this satire or does I must know context behind this comment???
stevedonovan · 1h ago
These are a few well-chosen dependencies for a serious project.
Rust projects can really go bananas on dependencies, partly because it's so easy to include them
obsoleszenz · 1h ago
The project only has 3 dependencies which i interpret as a sign of quality
kachapopopow · 1h ago
This looks rather similar to when I asked an AI to implement a basic xor problem solver I guess fundementally there's really only a very limited amount of ways to implement this.
abricq · 38m ago
This is great ! Congratulations. I really like your project, especially I like how easily it is to peak at.
Do you plan on moving forward with this project ? I seem to understand that all the training is done on the CPU, and that you have next steps regarding optimizing that. Do you consider GPU accelerations ?
Also, do you have any benchmarks on known hardware ? Eg, how long would it take to train on a macbook latest gen or your own computer ?
Charon77 · 1h ago
Absolutely love how readable the entire project is
emporas · 1h ago
It is very procedural/object oriented. This is not considered good Rust practice. Iterators make it more functional, which is better, more succinct that is, and enums more algebraic. But it's totally fine for a thought experiment.
koakuma-chan · 36m ago
It's AI generated
Revisional_Sin · 27m ago
How do you know? The over-commenting?
koakuma-chan · 19m ago
I know because this is how an AI generated project looks. Clearly AI generated README, "clean" code, the way files are named, etc.
magackame · 10m ago
Not sure myself. Commit messages look pretty human. But the emojis in readme and comments like "// Re-export key structs for easier access", "# Add any test-specific dependencies here if needed" are sus indeed.
cmrdporcupine · 15m ago
To me it looks like LLM generated README, but not necessarily the source (or at least not all of it).
Or there's been a cleaning pass done over it.
koakuma-chan · 10m ago
I think pretty clearly the source is also at least partially generated. None the less, just a README like that already sends a strong signal to stop looking and not trust anything written there.
GardenLetter27 · 12m ago
The repeated Impls are strange.
magackame · 6m ago
Where? Don't see any on latest main (685467e).
yieldcrv · 1h ago
Never knew Rust could be that readable. Makes me think other Rust engineers are stuck in a masochistic ego driven contest, which would explain everything else I've encountered about the Rust community and recruiting on that side.
GardenLetter27 · 10m ago
Most Rust code looks like this - only generic library code goes crazy with all the generics and lifetimes, due to the need to avoid unnecessary mallocs and also provide a flexible API to users.
But most people aren't writing libraries.
jmaker · 1h ago
Not sure what you’re alluding to but that’s just ordinary Rust without performance or async IO concerns.
ndai · 1h ago
I’m curious where you got your training data? I will look myself, but saw this and thought I’d ask. I have a CPU-first, no-backprop architecture that works very well on classification datasets. It can do single‑example incremental updates which might be useful for continuous learning. I made a toy demo to train on tiny.txt and it can predict next characters, but I’ve never tried to make an LLM before. I think my architecture might work well as an on-device assistant or for on-premises needs, but I want to work with it more before I embarrass myself. Any open-source LLM training datasets you would recommend?
huggingface has plenty of openai and antrophic user to assistant chains, beware there are dragons (hallucinations), but good enough for instruction training. I actually recommend distilling kimi k2 instead for instruction following capabilities.
enricozb · 1h ago
I did this [0] (gpt in rust) with picogpt, following the great blog by jaykmody [1].
// Increased for better learning
this doesn't tell me anything
// Use the constants from lib.rs
const MAX_SEQ_LEN: usize = 80;
const EMBEDDING_DIM: usize = 128;
const HIDDEN_DIM: usize = 256;
these are already defined in lib.rs, why not use them (as the comment suggests)
[1] https://github.com/astral-sh/uv
the difficulty of including a dependency should be proportional to the risk you're taking on, meaning it shouldn't be as difficult as it in, say, C where every other library is continually reinventing the same 5 utilities, but also not as easy as it is with npm or cargo, because you get insane dependency clutter, and all the related issues like security, build times, etc
how good a build system isn't equivalent of how easy it is include a dependency, while modern languages should have a consistent build system, but having a centralised package repository that anyone freely pull to/from, and having those dependencies freely take on any number of other dependencies is a bad way to handle dependencies
In my experience it's just bugs and poor decision making on the maintainers (eg. pytorch dropping support for intel mac, leftpad in node) or on the language and package manager developers side (py2->3, commonjs, esm, go not having a package manager, etc).
Cargo has less friction than pypi and npm. npm has less friction than pypi.
And yet, you just need to compromise one lone, unpaid maintainer to wreck the security of the ecosystem.
Its a very cool excercise, I did the same with Zig and MLX a while back, so I can get a nice foundation, but since then as I got hooked and kept adding stuff to it, switched to Pytorch/Transformers.
(the code looks like a very junior or a non-dev wrote it tbh).
Looking good!
yep, still looks relatively good.
Rust projects can really go bananas on dependencies, partly because it's so easy to include them
Do you plan on moving forward with this project ? I seem to understand that all the training is done on the CPU, and that you have next steps regarding optimizing that. Do you consider GPU accelerations ?
Also, do you have any benchmarks on known hardware ? Eg, how long would it take to train on a macbook latest gen or your own computer ?
Or there's been a cleaning pass done over it.
But most people aren't writing libraries.
For just plain text, I really like this one - https://huggingface.co/datasets/roneneldan/TinyStories
[0]: https://github.com/enricozb/picogpt-rust [1]: https://jaykmody.com/blog/gpt-from-scratch/