Once configured, Tor Hidden Services also just work (you may need to use some fresh bridges in certain countries if ISPs block Tor there though). You don’t have to trust any specific third party in this case.
Once configured, Tor Hidden Services also just work (you may need to use some fresh bridges in certain countries if ISPs block Tor there though). You don’t have to trust any specific third party in this case.
Don’t know much of the stochastic parrot debate. Is my position a common one?
In my understanding, current language models don’t have any understanding or reflection, but the probabilistic distributions of the languages that they learn do - at least to some extent. In this sense, there’s some intelligence inherently associated with language itself, and language models are just tools that help us see more aspects of nature than we could earlier, like X-rays or a sonar, except that this part of nature is a bit closer to the world of ideas.
Have been using llama.cpp, whisper.cpp, Stable Diffusion for a long while (most often the first one). My “hub” is a collection of bash scripts and a ssh server running.
I typically use LLMs for translation, interactive technical troubleshooting, advice on obscure topics, sometimes coding, sometimes mathematics (though local models are mostly terrible for this), sometimes just talking. Also music generation with ChatMusician.
I use the hardware I already have - a 16GB AMD card (using ROCm) and some DDR5 RAM. ROCm might be tricky to set up for various libraries and inference engines, but then it just works. I don’t rent hardware - don’t want any data to leave my machine.
My use isn’t intensive enough to warrant measuring energy costs.
It would. But it’s a good option when you have computationally heavy tasks and communication is relatively light.