llm.f90 VS mamba

I'd like to think he took the name from my llm.f90 project https://github.com/rbitr/llm.f90

It was originally based off of Karpathy's llama2.c but I renamed it when I added support for other architectures.

Probable a coincidence :)

I'm a Fortran hobbyist. I'm working (unfortunately less frequently now) on a LLM framework in Fortan: https://github.com/rbitr/llm.f90

Also, I should promote the code I wrote for running this. It runs models in ggml format, the one I made available is an older checkpoint though. It's easy to convert the newer one. And it's in Fortran but it should be easy to get gfortran if you don't have it installed.

https://github.com/rbitr/llm.f90/tree/optimize16/purefortran

The original mamba code has a lot of speed optimizations and other stuff that make it difficult to immediately get so this will help with learning.

I can't help but also plug my own Mamba inference implementation. https://github.com/rbitr/llm.f90/tree/master/ssm

You may also be interested in https://github.com/rbitr/llm.f90/tree/master/ssm it's my inference only implementation of mamba which ends up being much simpler than the training code in the original repo

> how the recall can grow unbounded with no tradeoff

this? https://github.com/state-spaces/mamba/issues/175

If you want to learn this stuff as a computer engineer, you can read the code here [0]. I find the math quite helpful.

[0]: https://github.com/state-spaces/mamba

This model was discussed recently: https://news.ycombinator.com/item?id=38522428 It's a new kind of ML model architecture that can be used instead of a transformer in LLMs.

See also the original repo from the paper: https://github.com/state-spaces/mamba

[2] - https://github.com/state-spaces/mamba

Out of curiosity, does anyone feel as though there's any benefit to linking to reddit when we can link to whatever the link is? I for one do not click the link and read discussion on reddit - if I wanted that sort of discussion, I would browse there, not HN.

The library is compatible with any auto-regressive model, not transformers. To prove our point we integrated Mamba, a new state-space model architecture, to the library. Try it out!

I ran the NanoGPT of Karparthy replacing Self-Attention with Mamba on his TinyShakespeare Dataset and within 5 minutes it started spitting out the following:

You might have come across the paper Mamba paper in the last days, which was the first attempt at scaling up state space models to 2.8B parameters to work on language data.