pyre-check VS mamba

Did you come across pyre in your search? MIT license and pretty fast.

https://github.com/facebook/pyre-check

Pyre is a performant type-checker developed by Facebook. Pyre can analyse codebases with millions of lines of code incrementally – providing instantaneous feedback to developers as they write code.

Pyre from Meta, pyright from Microsoft and PyType from Google provide additional assistance. They can 'infer' types based on code flow and existing types within the code.

Have you seen Pyre[0]? Not Rust, OCaml, and pretty fast. Made by a team at Meta and open sourced on GitHub. If you use python-lsp, I wrote an extension[1] to enable integration (though I haven't tested it recently, been programming in rust; it is mostly a "for me" extension).

0: https://pyre-check.org/

1: https://github.com/cricalix/python-lsp-pyre

Internally we use Pyre for Python type checking: https://github.com/facebook/pyre-check

Before type hinting, work had intense rules and linters enforcing docstrings with types. Now, type hints and automatic pyre runs take care of all the heavy lifting.

Python now has an optional type system and if you add one of them such as mypy or pyre to your CI process and you can configure GitHub to refuse the pull request until types are added you can make it somewhat strongly typed.

If you have a preexisting codebase I believe the way you can convert it is to add the types that you know on commits and eventually you will have enough types that adding the missing ones should be easy. For the missing ones Any is a good choice.

https://pyre-check.org and https://github.com/python/mypy are popular.

> 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.