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How does Lemmy feel about "open source" machine learning, akin to the Fediverse vs Social Media? (lemmy.world)

submitted 2 months ago* (last edited 2 months ago) by [email protected] to c/[email protected]

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Obviously there's not a lot of love for OpenAI and other corporate API generative AI here, but how does the community feel about self hosted models? Especially stuff like the Linux Foundation's Open Model Initiative?

I feel like a lot of people just don't know there are Apache/CC-BY-NC licensed "AI" they can run on sane desktops, right now, that are incredible. I'm thinking of the most recent Command-R, specifically. I can run it on one GPU, and it blows expensive API models away, and it's mine to use.

And there are efforts to kill the power cost of inference and training with stuff like matrix-multiplication free models, open source and legally licensed datasets, cheap training... and OpenAI and such want to shut down all of this because it breaks their monopoly, where they can just outspend everyone scaling , stealiing data and destroying the planet. And it's actually a threat to them.

Again, I feel like corporate social media vs fediverse is a good anology, where one is kinda destroying the planet and the other, while still niche, problematic and a WIP, kills a lot of the downsides.

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[–] [email protected] 1 points 2 months ago (2 children)

None taken! I'll check out AI Horde!

Is there any objective measured ways or at least subject reviews based metrics for a model on g8ve problem set? I know the white papers tend to include it and sometimes the git repos, but I don't see that info when searching through ollama for example.

I saw you other post about ollama alts and the concurrency mention in one of the projects README sounds promising.

[–] [email protected] 2 points 2 months ago

Oh, and as for benchmarks, check the huggingface open llm leaderbard. The new one.

But take it with a LARGE grain of salt. Some models game their scores in different ways.

There are more niche benchmarks floating around, such as RULER for long context performance. Amazon ran a good array of models to test their mistral finetune: https://huggingface.co/aws-prototyping/MegaBeam-Mistral-7B-512k

[–] [email protected] 2 points 2 months ago* (last edited 2 months ago)

Honestly I would get away from ollama. I don't like it for a number of reasons, including:

Suboptimal quants

suboptimal settings

limited model selection (as opposed to just browsing huggingface)

Sometimes suboptimal performance compared to kobold.cpp, especially if you are quantizing cache, double especially if you are not on a Mac

Frankly a lot of attention squatting/riding off llama.cpp''s development without contributing a ton back.

Rumblings of a closed source project.

I could go on and on, inclding some behavior I just didn't like from the devs, but I think I'll stop, as its really not that bad.