LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.
Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
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As far as I can tell, open-webui doesn't have a way to manage gguf models via llama.cpp / llama-swap. It can leverage the models just not a great experience managing (downloading, deleting, etc). I can definitely adjust my workflow to use another tool, it just means an additional hurdle compared to my current flow but it sounds like it's worth it from a performance standpoint
Eh, are you really using that many new models where sshing into your server / cding into your model folder, wgetting a url and configuring your interfaces every now and then is that much of an issue? I can't imagine using more than like one or two new models a month unless there's some insane string of releases or something.
Also, I mean, everyone's setup is different, but there's a significant amount of performance you're potentially leaving on the table by not using llama.cpp, potentially in the double-digit percentages. (Plus, if you have a fairly recent Nvidia setup and are willing to wait a bit for the latest models, ik_llama.cpp is a fantastic fork that I've found can get way better performance on most models than even llama.cpp.)