Submitted 9 months ago by hexual@lemmy.world to technology@lemmy.world
https://huggingface.co/abacusai/Smaug-72B-v0.1
Abacus.ai:
We recently released Smaug-72B-v0.1 which has taken first place on the Open LLM Leaderboard by HuggingFace. It is the first open-source model to have an average score more than 80.
i thought this was about the MUD server software, and got excited. alas.
Oh yay another model I can’t run on my computer :'(
That’s nice and all, but what are some FOSS models I can run on GPU with only 4GB?
I’ve tried Deepseek Coder, and it’s pretty nice for what I use it for. Then there’s TinyLlama, which… well it’s fast, but I need to be veeeery exact in how I prompt it.
4GB is practically nothing in this space. Ideally you want at least 10GB of dedicated vram if you can’t get even more. Keep in mind you’re also probably trying to share that vram with your operating system. So it’s more like ~3GB before you even started.
Kolboldcpp is capable of using both your GPU and CPU together, you might wanna consider that. (Using a feature called layers) There’s a trade-off that occurs between the memory available and the quality of its output and the speed of the calculation.
The model mentioned in this post can be run on the CPU with enough system ram or swap.
If you wanna keep it all on the GPU check out 4bit models. Also there’s been a lot of work into trying to do this with the raspberry Pi. I suspect that their work could help you out here as well.
Unfortunately LLMs need a lot of VRAM. You could try using koboldcpp, it runs on the CPU but let’s you offload layers onto the GPU. That way you might be able to stay withing those 4gb even with larger models.
I’m currently playing around with the Jan client, which uses the nitro engine. I think I need to read up on it more, because when I set the ngl value to 15 in order to offload 50% to GPU like the Jan guide says, nothing happens. Though that could be an issue specific to Jan.
Depends on your needs. Best look around in !localllama@sh.itjust.works or similar. (I don’t wanna say reddit but r/localLlama is much larger.)
If you’re more into creative writing, maybe look for places that discuss SillyTavern (r/SillyTavernAI is an option). It’s software for role-play chats, which may not be what you want. But the community is (relatively) large and likely to have good tips for non-coding/less technical applications.
Where can we all see the leader board?
If only I had the $ to get a rig that could run this locally
Since I had an okay experience with EasyDiffusion I tried running text gen locally through oobabooga, but no matter which model I load, it just crashes whenever it tries to generate anything, regardless if it runs through the UI's chat or SillyTavern. No error in the terminal either, it just stops and throws me back into the command line.
OOTL: What is a LLM and what does it do?
Large Language Model AI. Like ChatGPT.
And at 72 billion parameters it's something you can run on a beefy but not special-purpose graphics card.
What a catchy name.
It’s pronounced “Smaug”
Potato potato
simple@lemm.ee 9 months ago
I’m afraid to even ask for the minimum specs on this thing, open source models have gotten so big lately
TheChurn@kbin.social 9 months ago
Every billion parameters needs about 2 GB of VRAM - if using bfloat16 representation. 16 bits per parameter, 8 bits per byte -> 2 bytes per parameter.
1 billion parameters ~ 2 Billion bytes ~ 2 GB.
From the name, this model has 72 Billion parameters, so ~144 GB of VRAM
nicetriangle@kbin.social 9 months ago
Ok but will this run on my TI-83? It's a + model.
FaceDeer@kbin.social 9 months ago
It's been discovered that you can reduce the bits per parameter down to 4 or 5 and still get good results. Just saw a paper this morning describing a technique to get down to 2.5 bits per parameter, even, and apparently it 's fine. We'll see if that works out in practice I guess
Amaltheamannen@lemmy.ml 9 months ago
Though with quantisatiom you can get it down to like 30GB of vram or less.
rs137@lemmy.world 9 months ago
Llama 2 70B with 8b quantization takes around 80GB VRAM if I remember correctly. I’ve tested it a while ago.
OutrageousUmpire@lemmy.world 9 months ago
Any idea what 8Q requirements would be? Or 4 or 5?
General_Effort@lemmy.world 9 months ago
It’s derived from Qwen-72B, so same specs. Q2 clocks it in at only ~30GB.
SinningStromgald@lemmy.world 9 months ago
Just a data center or two. Easy peasy dirt cheapy.
girsaysdoom@sh.itjust.works 9 months ago
I think I read somewhere that you’ll basically need 130 GB of RAM to load this model. You could probably get some used server hardware for less than $600 to run this.
cm0002@lemmy.world 9 months ago
Oh if only it were so simple lmao, you need ~130GB of VRAM, aka the graphics card RAM. So you would need about 9 consumer grade 16GB graphics cards and you’ll probably need Nvidia because of fucking CUDA so we’re talking about thousands of dollars. Probably approaching 10k
Ofc you can get cards with more VRAM per card, but not in the consumer segment so even more $$$$$$
ArchAengelus@lemmy.dbzer0.com 9 months ago
Unless you’re getting used datacenter grade hardware for next to free, I doubt this. You need 130 gb of VRAM on your GPUs
L_Acacia@lemmy.one 9 months ago
Around 48gb of VRAM if you want to run it in 4bits