I’m not a huge fan of AI, but I do use it occasionally. I found a tool on Flathub that I’ve been trying out, so I thought i would share it if any for you need something like it. The interface is also pretty nice if you use Gnome(it don’t look so nice KDE). Its unstable on my arch install

      • @[email protected]English
        12•16 days ago

        Way more models available, faster in my experience, more reliable, local ChatGPT compatible api and advanced fine tuning features. There have been some additions to Alpaca since I last used it, so maybe I will try it again soon but since I don’t use it regularly I use GPT4ALL because it just works, and when I tried Alpaca didn’t.

        • @[email protected]
          5•16 days ago

          I’d say alpaca is pretty much identical these days, the only major difference is the interface. If you need more power running something like Open WebUI with Ollama makes more sense.

    • @[email protected]
      29•16 days ago

      local AI models are a thing you know, just run this on a computer with no internet access and you’ll swiftly see whether it relies on servers or not.

    • Match!!English
      13•16 days ago

      so like, turn your network connection off and try it

          • EverettEnglish
            3•16 days ago

            I can run Llama3 on my desktop with a 3060, answers are near instant.

        • Match!!English
          13•16 days ago

          model training is often exorbitantly high-energy and storage, but the model output is not: GPT3 cost $7 million dollars just in electricity costs to train, but the model is “only” 500 GB, and can be run locally on just CPU processing

        • @[email protected]English
          6•16 days ago

          You can run a basic model on pretty mid-range hardware, the smaller ones are only 1-2GB in size.

        • @[email protected]
          3•15 days ago

          To be fair, the first time I tried running local AI (and it actually worked), I was so surprised that I actually unplugged my Ethernet and tried again. I’m still surprised, but it’s possible for the massive amounts of training data to be compressed to a model under only 10 or 20 GB.