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I put up a draft PR to demo how to run it on a GPU: https://github.com/getumbrel/llama-gpt/pull/11
It breaks other things like model downloading, but once I got it to a working state for myself, I figured why not put it up there in case its useful. If I have time, I'll try to rework it a little bit with more parameters and less dockerfile repetition to fit the main project better.
https://github.com/jmorganca/ollama was extremely simple to get running on my M1 and has a couple uncensored models you can just download and use.
Very cool, this looks like a combination of chatbot-ui and llama-cpp-python? A similar project I've been using is https://github.com/serge-chat/serge. Nous-Hermes-Llama2-13b is my daily driver and scores high on coding evaluations (https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...).
Very cool, this looks like a combination of chatbot-ui and llama-cpp-python? A similar project I've been using is https://github.com/serge-chat/serge. Nous-Hermes-Llama2-13b is my daily driver and scores high on coding evaluations (https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...).
I like this for turn by turn conversations: https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b
this for zero shot instructions: https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-...
easiest way would be https://github.com/oobabooga/text-generation-webui
a little more complex way I do is I have a stack with llama.cpp server, a openai adapter, and bettergpt as frontend using the openai adapter as the custom endpoint. bettergpt ux beats oogaboga by a long way (and chatgpt on certain aspects)
Agreed.
Gpt4all[1] offers a similar 'simple setup' but with application exe downloads, but is arguably more like open core because the gpt4all makers (nomic?) want to sell you the vector database addon stuff on top.
[1]https://github.com/nomic-ai/gpt4all
I like this one because it feels more private / is not being pushed by a company that can do a rug pull. This can still do a rug pull, but it would be harder to do.
What is the advantage of this versus running something like https://github.com/simonw/llm , which also gives you options to e.g. use https://github.com/simonw/llm-mlc for accelerated inference?
What is the advantage of this versus running something like https://github.com/simonw/llm , which also gives you options to e.g. use https://github.com/simonw/llm-mlc for accelerated inference?