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.
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.
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
Ok but will this run on my TI-83? It’s a + model.
Only if it’s silver.
Dang. So close.
My 83 was ganked by some kid I knew so my folks bought me a silver. He denied it. I learned that day to write my name in secret spots.
That kid you knew was a dick. At least he taught you a valuable lesson, I guess.
He absolutely was a dick. I stopped being mates with him after that. My school was like “yeah the cameras didn’t work that day actually”
Leads me to believe that the cameras never actually worked.
I believe that. Or they just didn’t want to be responsible for dealing with theft. Both ways make perfect sense to me.
no. but put this clustering software i wrote in ti-basic on 40 million of them? still no
Ooooo gotta upgrade to the 86 my dude
Not what I was hoping to hear tbh
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
I’m more experienced with graphics than ML, but wouldn’t that cause a significant increase in computation time, since those aren’t native types for arithmetic? Maybe that’s not a big problem?
If you have a link for the paper I’d like to check it out.
My understanding is that the bottleneck for the GPU is moving data into and out of it, not the processing of the data once it’s in there. So if you can get the whole model crammed into VRAM it’s still faster even if you have to do some extra work unpacking and repacking it during processing time.
The paper was posted on /r/localLLaMA.
You can take a look at exllama and llama.cpp source code on github if you want to see how it is implemented.
Though with quantisation you can get it down to like 30GB of vram or less.
Llama 2 70B with 8b quantization takes around 80GB VRAM if I remember correctly. I’ve tested it a while ago.
Any idea what 8Q requirements would be? Or 4 or 5?
https://huggingface.co/senseable/Smaug-72B-v0.1-gguf/tree/main
About 44GB and 50GB for the Q4 and 5. You’d need quite some extra to fully use the 32k context length.