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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.