mamba VS PyInstaller

> 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.

Not sure if fixed in this patch, but pyinstaller had an issue in 3.12.0 https://github.com/pyinstaller/pyinstaller/issues/7992

You are right. I think I've misremembered the module name - it was uwsgi, not uvicorn.

This is a github issue where I discussed my original issue with PyInstaller devs - the dev explained the situation very well: https://github.com/pyinstaller/pyinstaller/issues/6362

PyInstaller seemed like the most maintained and developed tool to freeze python script into an executable, so I went with it. As expected, the freezed interface with the model was gigabytes large, so I had to figure out how to squeeze this. Fortunately, Onnx worked wonders and packaged the model into an inference only state, so I could throw away the Pytorch and Torchtext dependencies when freezing with Pyinstaller.Now the size of the executable with the model was 43MB instead of 4GB.

You might want to see Pyinstaller and auto_py_to_exe if you want a GUI interface.

It uses the pyinstaller command behind and please read their docs if you want to know more details.

I also found another link on github that may have some solutions to try: https://github.com/pyinstaller/pyinstaller/issues/3600

I made a Django react app. Now I want to make it a desktop application so that the user does not have type python manage.py runserver and also activate the environment every time. I used pyinstaller. I did all the steps mentioned for django

First, instead of creating a VM for windows, you may need to use a software called Wine mentioned in the pyinstaller FAQs