- cross-posted to:
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- cross-posted to:
- [email protected]
Running llama-2-7b-chat at 8 bit quantization, and completions are essentially at GPT-3.5 levels on a single 4090 using 15gb VRAM. I don’t think most people realize just how small and efficient these models are going to become.
[cut out many, many paragraphs of LLM-generated output which prove… something?]
my chatbot is so small and efficient it only fully utilizes one $2000 graphics card per user! that’s only 450W for as long as it takes the thing to generate whatever bullshit it’s outputting, drawn by a graphics card that’s priced so high not even gamers are buying them!
you’d think my industry would have learned anything at all from being tricked into running loud, hot, incredibly power-hungry crypto mining rigs under their desks for no profit at all, but nah
not a single thought spared for how this can’t possibly be any more cost-effective for OpenAI either; just the assumption that their APIs will somehow always be cheaper than the hardware and energy required to run the model
because it is godawful at:
one of the things that I like using as an example here is: just make it do something it isn’t currently trained on
e.g. try to make it render content in zulu or isixhosa or [insert list of thousands of things that the developers barely/never touch] - it’s near guaranteed to have been trained on a very, very narrow set of that subject (if anything at all)
“then just train it on more data” comes the refrain
you: “okay, find me sufficient data of that”
them: “it’s just a curation problem”
you: “then who will create that?”
the absolute very minimum of thinking beyond the second order just so entirely evades so many of these promptfans it’s astounding
edit: TIL lemmy doesn’t do single newlines well
I keep flashing back to eliezer being smug on Twitter about how good ChatGPT is at chess, and it turns out once you get past book openings and extremely well-documented games, it completely shits the bed and stops acting like it knows the rules of chess or even basic chess notation. and this is a very obvious outcome if you know how LLMs work, but most promptfans don’t