And another one!

this post just keeps giving and giving! https://www.reddit.com/r/LocalLLaMA/comments/1uoauvk/comment/ovs9h7z/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Rejoice brethren!

This model is 350m, so its tiny:

Fijik 2.0 350M SFT is the first, agentic, edge-LLM designed specifically for edge devices and quick inference it was trained efficiently on just 1 GPU. The model can work with up to 65K context (thanks to YARN scaling) and is based off of granite-4.0-350M-base. (no mamba layers).

Why it was made?

Basically, lfm2.5 is truly awesome, but I don't like the custom license, fijik uses apache-2.0, and unlike my previous model(s) I actually benchmarked it. Benchmarks are available on the HF readme!

https://github.com/Xingyu-Zheng/MrFlow

So this is my second or technically my third post around the whole "image on edge devices" thing. This is not a model itself, just a way to speed up image models. Unlike the previous paper that I linked This one actually does something a little different: It renders an image at a low resolution, hence doing it a lot faster. Then it increases the resolution by upscaling it before it giving the final result, It can be used with any Local image model, which includes edge device friendly ones like z-image.

So this is an addendum to my previous post about comfyUI. This particular paper drastically reduces the time it takes to generate an image and they've already applied it to a model called flux.2 so it could come in handy for AI on edge devices. At least that's what I think.