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.
Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.
As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.
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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Hi, sure, thank you so much for helping out! As for LLaMA, I would point you at llama.cpp, (https://github.com/ggerganov/llama.cpp) which is the absolute bleeding edge, but also has pretty useful instructions on the page (https://github.com/ggerganov/llama.cpp#usage). You could also use Kobold.cpp, but I don't have any experience with it, so I can't help you if you have issues.
Adding to this: text-generation-webui (https://github.com/oobabooga/text-generation-webui) works with the latest bleeding edge llama.cpp via llama-cpp-python, and it has a nice graphical front-end. You do have a manually tell pip to install llama.cpp-python with the right compiler flags to get GPU acceleration working but the llama-cpp-python github and ooba github explain how to do this.
You can even set up GPU acceleration through metal on m1 Macs I've seen some fucking INSANE performance numbers online for the higher RAM MacBook pros (20+ tokens/sec, I think with a 33b model, but it might have been 13b, either way, impressive.)