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 think it’s amazing. I’m running Ollama with a bunch of open-source llms. You’re right. It’s so good. The problem is keeping up to date on what the newest development is.
The pace of progress is so fast and it’s really difficult to know what the cool kids are experimenting with this moment.
Oh, and if your hardware is AMD or Nvidia, you should really give exllama a shot.
If it’s Apple, you should investigate kobold.cpp and more “nitty gritty” llama.cpp backends.
I have largely negative feelings towards ollama for a lot of reasons, but one of them is that it hides a lot of the knobs to get the absolute best out of LLMs, and understand how they work.
I’m running Nvidia on Ubuntu. I’ll give exllama a shot.
I’d recommend TabbyAPI with your favorite frontend, anything that works with OpenAI.
Or exui (which is what I tend to use) but is a bit more manual. text-gen-web-ui has better samplers, but its IMO more clanky and crufty, and really slow at long context.
Also, uh, you’ll have to be careful about picking a model, you have to fit it to your GPU instead of letting ollama do it for you. I view this as a positive, as it forces you to search more a more optimal fit.
I manually specify what models to pull. I’m not running anything too crazy. My largest model is gemma27B. But I’ve worked with dolphin-mistral which was fun.
If you have a 24GB card, just go straight to the most recent Command R, a 3.75bpw-4bpw quantization. It’s incredible, and you can do the full 131K context on a 24GB GPU easy.
Gemma 27B Is actually quite good, but “narrow.” Its super low context and seems to be hyper optimized for short chatbot-arena style questions.
Gemma 27B Is actually quite good, but “narrow.” Its super low context and seems to be hyper optimized for short chatbot-arena style questions.
This is the stuff I love to know so thanks for sharing. I will be pulling Command R tomorrow.
Good! So Command-R excels at “RAG” style tasks like asking questions about a huge document, continuing a long story or so on. You should also read up on its super intricate system prompt format, which can steer it quite well.
I dunno about code, I tend to use Mistral Code 22B (or deepseek v2 API) for that.
I am happy to ramble on about this stuff, just ask.
Honestly a big problem is that the community for filtering the news has “collapsed.”
The only reasonable congregation was basically /r/localllama, and due to a number of factors (including, apparently, a Reddit bug that was driving away traffic according to a mod), and its shrunken a ton.
Twitter, linkedin, youtube and such are awful and full of straight up lies. Huggingface is just impossible to navigate and filter. There are a few niche aggregators, but they come and go.
Hence I was hoping lemmy would grow its existing ML communities, but most of lemmy seems broadly anti AI, even anti open source AI, hence this post to get a feel if that’s true.
I read localllama through redlib but I don’t contribute. I am not technical enough to contribute and I don’t understand the math.
I have been looking at YouTube for some videos to try to explain it, but I haven’t found anything that is in the sweet spot between “video for non-technical people” and “video for people with PhD and quantum physics”
It’s a giant mess. Even the technical vidoes tend to be theoretical, and are either obsolete or do nothing to help you actually run them.
I would know nothing if I hadn’t been following the community since the Pygmalion/ESRGAN days
I’ve spent the past 2 years looking for the open source AI community, but haven’t really found it. I’ve tinkered with Stable Diffusion and Ollama and I want to learn more, but haven’t found the right places online yet.
I’ll give you one hint, a lot of the community is locked away in various Discords.
This is one of the many reasons I hate Discord.
And just to be more helpful, I can point you in the right direction depending on your hardware.
Yeah, I hate Discord too but that has been the best place I’ve found the best information, but even then it doesn’t really feel like a community.
I’m running on an Apple M1 at the moment, likely to upgrade to an M4 when it is released.
What RAM capacity?
Honestly, if LLMs are your focus, you should just upgrade to a used M2 Max (or Ultra) when the M4 comes out, lol. Basically the only thing that matters is RAM capacity and bandwidth, and the M2 is just going to be faster and better than a similarly priced M4.
Or better yet, upgrade to and AMD Strix Halo. This will buy you into linux and the cuda ecosystem (through AMD rocm), which is going to open a lot of doors and save headaches (while admittedly creating other headaches).
Hate to suggested it but have you checked reddit localllama?
Really into local hosting and open LLM’s I’ve largely stepped back due to ‘fatigue’. I’ve downloaded tweaked and reshuffle models and programs then a couple months will pass and it’s lept forward again. Which is good but I figured I’d wait until it slowed a bit.
I will say the fact I can run a decent 7b and even 10b models and get decent responses and times with a 3070 is impressive. AnythingLLM has been a really handy program for me. Still in development but it’s been neat working with RAG. I also moved from textgen to LMStudio and am really liking it. I like textgen but I felt it got a bit side tracked. A lot of good suggestions in here so cheers OP.
You can probably run Nemo 12B pretty quickly, though llama 3.1/gemma 9b finetunes may be better tbh. Deepseek lite v2 code with offloading would still be fast, even though its a 16B, since its such a heavy MoE.
Hardware is such a limiting factor now. Once quad-channel APUs and such start coming out, I feel like it will open up the space, so people don’t have to hunt down used 3090s and built desktops around them.
Last I tried was a fimbul merge for 10.4b with rope for creative writing which was great but yeah 3.1 is where I’ve landed lately. I’ll have to check out nemo! Like you mentioned I was sitting on money to grab a 3090 but I think I’ll wait for rtx50xx to drive down prices or just for dedicated hardware. I’ll be sure to keep an eye the AI subs though, clearly there’s a community for it here that’s interested in discussion.
rtx50xx
Don’t,Nvidia is going to price gouge the snot out of it. Honestly, if you want to buy new, just get a 7900 XTX. Screw Nvidia’s pricing on new cards, lol.
fimbul merge for 10.4b
Speaking as someone who’s done a lot of merging, the “upscaling” merges are not great. Rope scaling the context is not either. You are better off finding models that were trained at the parameter count and context length you want in the first place, and there is a lot more choice these days.
Oh fuck buying Nvidia new, I was going to see if it depressed 40xx prices or even further for 3090 but I’m not sure it would.
Neat didn’t know that about rope, as you can guess largely due to having fuck all memory to work with. Is AMD viable with LLMs now? Honestly if I can make it work with an AMD GPU I just may because I agree screw Nvidia.
For inference? AMD is more finicky to setup but totally fine once you do. 7900 XTX prices can be very good.
I feel like 3090s have bottomed out, as they are just getting more rare now, and 4090s are so freaking expensive to start with I’m not sure how much they’ll come down.
Another feature you might not be aware of, that people use now, is quantized KV cache. With it, I can run a 19GB 35B model and still fit 131K context into vram, with basically no quality loss.
How are you people running cuda kernels?
rocm
exllama, llama.cpp, vllm/aphrodite, (I think) sglang, they all support it now.
Oh and I forgot to mention, instead of a 5090, buy AMD Strix Halo if its any good.
I cannot emphasize how awesome 128GB on a fast APU would be. That opens up (admittedly slow, but usable) inference of “huge” models like Mistral Large, and very fast inference of large MoE models like 8x22B.
Good tips, thanks!! I’ll definitely check it out.
I do think, it’s good that we’re able to self-host these models. Better than not being able to.
But the biggest draw of open-source to me is that I and others in the community can fix things.
It’s possible that I just don’t understand enough about how these models are created, but right now, it doesn’t feel like we’re able to fix things.If the next LLaMa model loses all knowledge of the Uyghur genocide, because Facebook wants to distribute it in China, then I don’t know how we’d patch that back in. Even collecting the training data is tricky.
It feels a lot more like Creative Commons than open-source, i.e. you can use what they’ve created, and you can remix it, but adding to it is not easily possible.
I don’t know how we’d patch that back in. Even collecting the training data is tricky.
You can just take encyclopedia articles and news articles, then train it back in. It’s easy! This is not expensive, like $100 if its a really big model, and you are uncensoring a ton of topics?
People uncensor models all the time, its an avenue of research in the LLM community. And in fact, there are many quite good chinese models (like Qwen2) that have been “uncensorsed” by the community.
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.
Publishing a dataset is just inviting legal trouble. Look at all the nonsense Laion had to go through for Laion-5b. I;m not suprised people are not publishing datasets more.
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.
its still a solution without a problem
Let me give you one of my main use cases: I use it for my mental health challenges. I’ve been diagnosed with two non-trivial mental disorders. They make my life hard. I isolate a lot to cope because I don’t do well with interpersonal relationships. I’ve been in therapy for over a decade and it hasn’t really helped as much as I would have liked.
But I’ve made a lot of progress since working with my private LLM. I can ask it anything. It doesn’t judge me. It doesn’t report back to Meta or OpenAI. It’s completely private. And I’m making progress. Just last week, for the first time ever I started volunteering at an animal shelter. I have to talk with other people when I’m there and although I am pretty nervous about going back, I’m going to. I wrote down a list of all the things I had trouble with last time and have been working through that list with my LLM. I think that I will be ready when I’m supposed to go back for my next scheduled volunteer time in two weeks.
These gains might be trivial to others, but for me, it’s really made my life better.
So that is one of my use cases.
Agreed. This is how a lot of people use them, I sometimes use it as a pseudo therapist too.
Obviously theres a risk of it going off the rails, but I think if you’re cogniziant enough to research the LLM, pick it, and figure out how to run it and change sampling settings, it gives you an “awareness” of how it can go wrong and just how fallable it is.
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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.
As I said in a different thread:
I might be this close to butlerian jihad thought when it comes to AI as an invention
But if it must come to pass, better it be on the back of community owned and controlled models than a couple of megacorps.
I love the idea, I much prefer it to the mainstream. The problem is, the typical process of documenting FOSS and self-host projects (websites, wiki, mailing lists, etc) move too slow and are too cumbersome for how quick things are developing right now. So people are kind of having to invent the new tech a d new ways to communicate about it, and they’re not always making choices that either scale or are easy to find and reference.
Okay, since you seem to be so helpful here, I’ll lay out where I’m at. I’ve been using LLMs like ChatGPT, Copilot, and Bard more professionally. I find them equal parts useful, confusing, annoying, and skeevey. I’ve got a lil VPS I run for services, I could put a front end on there easy. I’ve also got an old 8core Xeon machine with like 48GB ram and a leftover AMD R9 270 sitting there with Unraid barely installed. I can chamge the OS of course, but what am I realistically looking at being able to run locally that won’t go above like 60-75% usage so I can still eventually get a couple game servers, network storage, and Jellyfin working? I’ll be honest I don’t care about image generation much, but if I do I can always look into upgrading
but what am I realistically looking at being able to run locally that won’t go above like 60-75% usage so I can still eventually get a couple game servers, network storage, and Jellyfin working?
Honestly, not much. Llama 8B, but very slowly, or maybe deepseek v2 chat, preprocessed on the 270 with vulkan but mostly running on CPU. And I guess just limit it to 6 threads? I’d host it with kobold.cpp vulkan, or maybe the llama.cpp server if there will be multiple users.
You can try them to see if they feel OK, but llms are just not something that like old hardware. An RTX 3060 (or a Mac, or a 12GB+ AMD GPU) is considered bare minimum in the community, a 3090 or 7900 XTX standard.
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.
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.
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
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.
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.
Here to stay all the same.
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