The "device" in question must be Apple Silicon because the `mlx` package is a hard dependency, or at least an ARM machine (I do not have any Apple Silicon Macbooks or ARM machines to run this). I tried tweaking this before realizing calls to this library is littered all over the repo. I don't really understand the AI ecosystem very well but it seems that the use of the `mlx` library should be supplanted by some other library depending on the platform machine. Until then, and the actual release of the iOS code somewhere, "everyday devices" is limited to premium devices that almost no one has more than one of. I'm looking forward to run this on other machine platforms and squeeze out what I can from old hardware laying around. Otherwise I doubt the tagline of the project.
Edit: to add on, the only evidence that this runs anywhere but Apple Silicon is the maintainer's Twitter where they show it running on two Macbook Pros as well as other devices. I'm not sure how many of those devices are not ARM.
I'm not throwing shade at the concept the author is presenting, but I'd appreciate if they could slow down functional commits (he is writing them right now as I type) and truthfully modify the documentation to state which targets are actually able to run this.
Edit: to add on, the only evidence that this runs anywhere but Apple Silicon is the maintainer's Twitter where they show it running on two Macbook Pros as well as other devices. I'm not sure how many of those devices are not ARM.
I'm not throwing shade at the concept the author is presenting, but I'd appreciate if they could slow down functional commits (he is writing them right now as I type) and truthfully modify the documentation to state which targets are actually able to run this.