LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
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I've never used oobabooga but if you use llama.cpp directly you can specify the number of layers that you want to run on the GPU with the -ngl flag, followed by the number.
So, as an example, a command (on linux) from the directory you have the binary, to run its server would look something like:
./llama-server -m "/path/to/model.gguf" -ngl 10
Another important flag that could interest you is -c for the context size.
This will put 10 layers of the model on the GPU, the rest will be on RAM for the CPU.
I would be surprised if you can't just connect to the llama.cpp server or just set text-generation-webui to do the same with some setting.
At worst you can consider using ollama, which is a llama.cpp wrapper.
But probably you would want to invest the time to understand how to use llama.cpp directly and put a UI in front of it, Sillytavern is a good one for many usecases, OpenWebUI can be another but - in my experience - it tends to have more half baked features and the development jumps around a lot.
As a more general answer, no, the safetensor format doesn't directly support quantization, as far as I know
Thank you for this!