That would actually be insane. Right now, I still need my GPU and about 8-10 gigs of VRAM to run a 7B model tho, so idk how that’s supposed to work on a phone. Still, being able to run a model that’s as good as a 70B model but with the speed and memory usage of a 7B model would be huge.
I only need ~4 GB of RAM/VRAM for a 7B model, my GPU only has 6GB VRAM anyway. 7B models are smaller than you think, or you have a very inefficient setup.
llama2 gguf with 2bit quantisation only needs ~5gb vram. 8bits need >9gb. Anything inbetween is possible. There are even 1.5bit and even 1bit options (not gguf AFAIK). Generally fewer bits means worse results though.
That would actually be insane. Right now, I still need my GPU and about 8-10 gigs of VRAM to run a 7B model tho, so idk how that’s supposed to work on a phone. Still, being able to run a model that’s as good as a 70B model but with the speed and memory usage of a 7B model would be huge.
I only need ~4 GB of RAM/VRAM for a 7B model, my GPU only has 6GB VRAM anyway. 7B models are smaller than you think, or you have a very inefficient setup.
That’s weird, maybe I actually am doing something wrong. Is it because I’m using GGUF models maybe?
llama2 gguf with 2bit quantisation only needs ~5gb vram. 8bits need >9gb. Anything inbetween is possible. There are even 1.5bit and even 1bit options (not gguf AFAIK). Generally fewer bits means worse results though.
I have never worked on machine learning, what does the B stand for? Billion? Bytes?
I think it’s how many billion parameters the model has
Thanks!