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> if these models are trained on essentially raw WWW sourced data, I would personally want raw output. Llama is a very high-quality foundation LLM, you can alr

> if these models are trained on essentially raw WWW sourced data, I would personally want raw output. Llama is a very high-quality foundation LLM, you can already run it very easily using llama.cpp and will get the raw output you need. https://github.com/ggerganov/llama.cpp https://github.com/ggerganov/llama.cpp There's already instructions on how anyone can fine-tune it to behave similarly to ChatGPT for as little as $100: https://crfm.stanford.edu/2023/03/13/alpaca.html https://crfm.stanford.edu/2023/03/13/alpaca.html

b33j0r 4y ago
“Easily” was a minor canard, or at least… it took me a couple of efforts over a couple of days to get the dependencies to play as nicely as “someone with a brand new M2 arm laptop.” If nothing else, I continue to be amazed and how uninteroperable certain technologies are. I had to remove glibc and gcc to get llama to compile on my intel macbook. Masking/hiding them from my environment didn’t work, as it went out and found them and their header files instead of clang. Which eventually worked fine.
- b33j0r 4y ago
  Well. I guess I’ll never understand why someone would dislike a comment about an experience different from theirs In a forum like this. i’m confused why someone would hate my report of how I had to solve a problem in my circustances. I hope to learn someday.
  - tarruda 4y ago
    I did not dislike your comment. The reason I considered easy was because I have very little knowledge in this area, in fact this is the first time I ever ran a machine learning model on my computer. I could not do it with the unmodified pytorch model (my GPU is not powerful enough to run even the 7B model), but I was surprised on how easy it was running with llama.cpp. I literally just followed the steps in the github page. But I was biased in saying it was easy, since I do have other knowledge (such as C development on Linux) which helped me.

b33j0r 4y ago

“Easily” was a minor canard, or at least… it took me a couple of efforts over a couple of days to get the dependencies to play as nicely as “someone with a brand new M2 arm laptop.” If nothing else, I continue to be amazed and how uninteroperable certain technologies are. I had to remove glibc and gcc to get llama to compile on my intel macbook. Masking/hiding them from my environment didn’t work, as it went out and found them and their header files instead of clang. Which eventually worked fine.

b33j0r 4y ago
Well. I guess I’ll never understand why someone would dislike a comment about an experience different from theirs In a forum like this. i’m confused why someone would hate my report of how I had to solve a problem in my circustances. I hope to learn someday.
- tarruda 4y ago
  I did not dislike your comment. The reason I considered easy was because I have very little knowledge in this area, in fact this is the first time I ever ran a machine learning model on my computer. I could not do it with the unmodified pytorch model (my GPU is not powerful enough to run even the 7B model), but I was surprised on how easy it was running with llama.cpp. I literally just followed the steps in the github page. But I was biased in saying it was easy, since I do have other knowledge (such as C development on Linux) which helped me.