Hoping that someone will shepherd the cause of merging the two; I think I'm too out of the loop to do it this time around :-)
Which I'm happy for. So given that decision, I don't think it's unreasonable to think that they might be open to including Mmproj files in the GGUF.
Only issue I can think of is, which one? BF16, F16? Etc
just about every field has documented cancellation of my egalitarian work for the apron brethren. it would be nice if this species could explain how to use a text to image without leaving which mess of 30g worth of downloads to try.
please, just someone SAY what things i need just once simply without going "you need 5G 5G 5G 5G 5G 5G"
your species doesn't work since the Emm Kay heterodyning from orbit. since rely, natural kinda ears to west papua FOR A REASON
Good lord, they managed to invent a format that is even less readable than XML.
Not sure what the solution is, other than writing a DSL to describe the model graphs which you then embed in the GGUF. The other fallback is to just read the PyTorch modules from the official model releases and convert that to GGML ops somehow.
I'd still love to see this, but it would need a cheerleader very familiar with the current state of the GGML IR.
Funny, to me AI models have "always" been single files, as that's what has been the norm in the local image gen business. Safetensors files allow stuffing all kinds of stuff inside them too, no GGUF needed for that. Though given that the text encoders of modern models are multi-gigabyte language models themselves, nobody includes redundant copies of those in every checkpoint.
That doesn't even make sense in the "local image gen business", you don't use a single weights file, you need a bunch of encoders/decoders and what not to actually be able to run the architecture with the weights.
Maybe the tooling you use hides those things from you, but they're still there under the surface.
As someone who is tinkering with a desktop-based inference app in FLTK[0], i wish this used the actual Jinja2 template parser llama.cpp uses (or there was another C function that did that since AFAICT for "proper" parsing you need to be able to pass a bunch of data to the template so it knows if you, e.g., do tool calling). Currently i'm using this adhocky function, but i guess i'll either write a Jinja2 interpreter or copy/paste the one from llama.cpp's code (depending on how i feel at the time :-P).
But yeah, GGUF's "all-in-one" approach is very convenient. And i agree that it feels odd to have the projection models as separate files - i remember when i first download a vision-capable model, i just grabbed whatever GGUF looked appropriate, then llama.cpp told me it couldn't do model and took me a bit to realize that i had to download an extra file. Literally my thought once i did was "wasn't GGUF supposed to contain everything?" :-P
Try both in lm studio, they really are surprisingly capable
Tried all the stuff bios, volting
I love TheBloke I wish he still made stuff
I didn't want to get personal with an LLM unless it was local so that's why I was setting this up but yeah. So far just research is what I was looking at.
They're mostly aimed at role play and sillytavern, but they're still generally good models, with lots of quants available
That means that every foundational model architecture requires new code in whatever is consuming the gguf to support that model.
hmmm...
