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.
I’m in favor of a “ML-GPL”, where models must be made available for free to those whose data was used to train them.
Practically that just means “open weights” lol. Easier to just do that than track all the sources.
Not that I disagree.
But one sticking point is allowing commercial use, as many companies do like noncommercial licenses so they can make money off them.
Open source is good and important, but its still a solution without a problem.
And even if you get to a point where performance without large dedicated machines is acceptable, it’s still a power drain.
I dunno, I keep a 35B open on my desktop all day just to bounce ideas off it, ask it stuff, easy queries, like a instant personal assistant.
And the feel is totally different when its yours. Long context responses on huge documents are instant because it’s cached, and I can repeat quieries over and over again without any worry. I can dig in and mess with the system prompt ,even the manual formatting, in ways that API models just don’t like. I can finetune smaller models for styles, thoug I don’t do this a ton. And I don’t feel weird about sending certain things over the internet to be datamined.
The visual media models tend to be more for crude entertainment, yeah.
Matmul free LLMs are theoretically incredibly power efficient, if accelerators for them ever come out.
Yes clearly you don’t know, thanks for that tidbit about yourself.
I heard you, and I disagree, I think they’re already useful and can be very reasonably power efficient.
OK, so the reaction here seems pretty positive.
But when I bring this up in other threads (or even on Reddit in the few subreddits I still use) the reaction is overwhelmingly negative. Like, I briefly mentioned fixing the video quality issues of an old show in an other fandom with diffusion models, and I felt like I was going to get banned and doxxed.
I see it a lot here too, in any thread about OpenAI or whatever.
Very much pro Open Source AI. Especially as a concept digital public good. With https://petals.dev/ being the most promising option that regard (imagine something like RAG for the arch wiki with very large models supported by the community!).
It feel very enthusiasts right now. Where I feel like I’m just on the cusp of having usable set up.
I personally really want a full Dev that just takes gitlab issues and runs codes against tests until it passes, and then cycles between attempting to explain what it doing and refactoring until that explanation is reasonably simple, then submit PR.
At the moment I am trying to use it as a copilot (ollama lama3, continue, and devonAI vscode plugins) all on my MacBook (my Linux machine were too small gpu wise, at least first time I attempted). That said it ok for questions no real luck on a decent experience for actually making anything.
The next step to me for it to move from enthusiast to hobbiest would be:
- Models that just work on my machine. I had to do a lot of trial and error just get performant models.
- Models just my use case. I don’t know what model support tooling, or multimodal inputs. What models are actually optimized for programing, for actions (ala openinterpretor), for reviewing documents, etc.
- For federated (like pedals.dev) I feel like I need some sane data guardrails. I don’t want my medical documents anywhere near “bittorrent style” anything, but would absolutely love to leverage it for better outcome on opensource projects without secrets file. This also feeds into point 2 to me.
- More sane RAG. Maybe even IPFS links to caches or DBs for popular data sources (like code docs for example).
I feel like there has to be a better way for this. Maybe its just selinux rules for data tags for locking down my local system and some routing config file at the root of my projects. Idk tbh
Honestly I am not sold on petals, it leaves so many technical innovations behind and its just not really taking off like it needs to.
IMO a much cooler project is the AI Horde: A swarm of hosts, but no splitting. Already with a boatload of actual users.
And (no offense) but there are much better models to use than ollama llama 8b, and which ones completely depends on how much RAM your Mac has. They get better and better the more you have, all the way out to 192GB. (Where you can squeeze in the very amazing Deepseek Code V2)
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.
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
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.
The splitting is 80% of the cool factor for me. Rather than bog down the one node that can handle those cooler models, and have more contribution opportunities.
I wonder honestly if a petals network could be a target host on horde lol
The problem is that splitting models up over a network, even over LAN, is not super efficient. The entire weights need to be run through for every half word.
And the other problem is that petals just can’t keep up with the crazy dev pace of the LLM community. Honestly they should dump it and fork or contribute to llama.cpp or exllama, as TBH no one wants to split up LLAMA 2 (or even llama 3) 70B, and be a generation or two behind for a base instruct model instead of a finetune.
Even the horde has very few hosts relative to users, even though hosting a small model on a 6GB GPU would get you lots of karma.
The diffusion community is very different, as the output is one image and even the largest open models are much smaller. Lora usage is also standardized there, while it is not on LLM land.
I guess to me be able to serve the 408b model even though I’m on a laptop is just awesome to me.
Also I saw Lora was an option for Petals but I haven’t messed with it at all.
I’m against all AI.
Here to stay all the same.
This is fair. So much about it is awful, even with more “open” AI.
But my counter argument is it’s happening anyway. And would you rather be stuck with Fediverse, or Facebook? Because if everyone keeps opposing all AI, we’re gonna be stuck with AI Facebook.
I’ll put it this way. When I call a company customer service, and they say “in a few words, tell us your issue”, what I do is say BLARHVSYKKUCAHN
And they say “I’m sorry. I didn’t understand that. Please state the reason for your call.”
And again I say “AJNCTHDTKVFRIDJXRI”
And they say “I’m sorry. I didn’t understand that. Please state the reason for your call.”
And I say “JCFYHCTJCZUIVDJ”
at this point, they either hang up on me, in which case I go see them in person.
OR
They say “I’m having trouble understanding you. Please wait while I connect you to someone who can help.”
The reason I do this is because I want to slow any advancement of any AI service, and fill them with garbage data.
And since the 90s I never use my real name online. If I’m signing up for something at Walmart, my name is Bob Wallemarte. Just enough to slip by their automated reject systems, but enough that if I start getting spam for Bob Wallemarte, I know Walmart sold my information.
Then when I sign up for something in the future, I use Walmarts local store address as my home address. So when Walmart wants to mail me spam, they mail it to themselves.
…In that case, shouldn’t you be OK with offline models? No data harvesting is a benefit.