Comment on No, you don't need a 'very bespoke AOSP' to turn your phone into a Rabbit R1 — here's proof

GlassHalfHopeful@lemmy.ca ⁨6⁩ ⁨months⁩ ago

• db2@lemmy.world ⁨6⁩ ⁨months⁩ ago

  You won’t like anything on offer currently except for that which is entirely self hosted.

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  • GlassHalfHopeful@lemmy.ca ⁨6⁩ ⁨months⁩ ago

    What are a couple of the best self hosted options? I wouldn’t mind giving it a go on my server. I may not have enough juice with what I currently run, but perhaps as a proof of concept first and then new hardware later.

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    • hedgehog@ttrpg.network ⁨6⁩ ⁨months⁩ ago

      I haven’t used it and only heard about it while writing this post, but Open WebUI looks really promising. I’m going to check it out the next time I mess with my home server’s AI apps. If you want more options, read on.

      Disclaimer: I’ve looked into most of the options below enough to feel comfortable recommending them, but I’ve only personally self hosted the Automatic 1111 webui, the Oobabooga webui, and Kobold.cpp.

      If you want just an LLM and an image generator, then:

      For the image generator, something that leverages Stable Diffusion models:

      • Automatic1111
      • InvokeAI
      • ComfyUI

      And then find models that you like at Civitai.

      For the LLM, the best option depends on your hardware. Not knowing anything about your hardware, I recommend a llama.cpp based solution. Check out one of these:

      • Oobabooga webui
      • Ollama
      • Jan
      • LMStudio
      • Kobold.cpp

      Alternatively, VLLM is allegedly the fastest for multi-user CPU-based inference, though as far as I can tell it doesn’t have its own webui (but it does expose OpenAI compatible API endpoints).

      And then find a model you like at Huggingface. I recommend finding a model quantized by TheBloke.

      There are a couple communities not on Lemmy that discuss local LLMs - r/LocalLLaMA and r/LocalLLM for example - so if you’re trying to figure out which model to try, that’s a good place to check.

      If you want a multimodal AI, you can use llama.cpp with a model like LLAVA. The options below also have multimodal support.

      If you want an AI assistant with expanded capabilities - like searching your documents or the web (RAG), etc. - then I don’t have a ton of experience there, but these seem to do that job:

      • H2OGPT
      • Open WebUI, formerly Ollama Webui

      If you want to use your local model as more than just a chat bot - integrating it into your IDE or a browser extension - then there are options there, and as far as I know every LLM above can be configured to expose an API allowing it to be used by your other tools. Some, like Open WebUI, expose OpenAI compatible APIs and so can be used with tools built to be used with OpenAI. I don’t know of many tools like this, though - I was surprisingly not able to find a browser extension that could use your own API, for example. Here are a couple examples:

      • Continue for VS Code / JetBrains IDEs
      • "ChatGPT Utilities" for A1111 WebUI - this doesn’t let you set the URL by default, but since it’s open source you could modify it pretty easily

      Also, I found this Medium article listed some of the things I described above as well as several others that I’d never heard of.

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      • GlassHalfHopeful@lemmy.ca ⁨6⁩ ⁨months⁩ ago

        Incredible resource! Thanks much. I’m looking forward to playing a bit and seeing how far I can push the hardware I currently have.

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      • Andromxda@lemmy.dbzer0.com ⁨6⁩ ⁨months⁩ ago

        Thanks for this detailed and informative comment!

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    • bassomitron@lemmy.world ⁨6⁩ ⁨months⁩ ago

      That’s a deep rabbit hole (no pun intended). I know it’s blasphemy to mention the other site around here, but check out the r/locallama subreddit. It covers more models than just LLaMA. There are literally thousands of variations at this point, so preferences are quite subjective based on your use case and your best bet is just to begin researching on your own for your intended purposes and available resources.

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      • balder1991@lemmy.world ⁨6⁩ ⁨months⁩ ago

        Yeah, the best way out of it is to get a few of the most recommended ones and test by yourself.

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• andrew0@lemmy.dbzer0.com ⁨6⁩ ⁨months⁩ ago

  What db2 already said. Microsoft just released Phi-3 mini, which could, allegedly, run locally on newer smartphones.

  If I understood correctly, the Rabbit thingy just captures your information locally and then forwards it to their server. So, if you want more power, you could probably do the same by submitting the same info to a bigger open source model than Phi-3, like Llama 3, hosted on your homelab. I believe you can set it up with huggingface/gradio, which sort of provides an API that you could use.

  That way, you don’t need a shitty orange box, and can always get the latest open source models with a few lines of code. There are plenty of open source frameworks in the works at the moment, and I believe that we’re not far off from having multi-modal LLMs running on homelab-level hardware (if you don’t mind a bit of lag).

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  • GlassHalfHopeful@lemmy.ca ⁨6⁩ ⁨months⁩ ago

    huggingface! i found it once and never could remember again who hosted all those models. thank you!

    i use a different device than homelab, but now I am curious what I may be able to achieve with my syno system. it’s hw weak, so probably not a lot. but i would like to give it a go. if it’s decent, i may consider another device for the purpose.

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    • andrew0@lemmy.dbzer0.com ⁨6⁩ ⁨months⁩ ago

      Good luck! You can try the huggingface-chat repo, or ollama with this web-ui. Both should be decent, as they have instructions to set up a docker container.

      I believe the Llama 3 models are out there in a torrent somewhere, but I didn’t dig to find it. For the 70B model, you’ll probably need around 64GB of RAM available, but the 7B one should run fine with just 8GB. It will be somewhat slow though, compared to the ChatGPT experience. The self-attention mechanism can be parallelized, which is why you will see much better results on a GPU. According to some others that tested it, if you offload some stuff to RAM, you could see ~10-12 tokens per second on an RTX 3090 for certain 70B models. But more capable ones will be at less than 1 token per second, all depending on the context window you use.

      If you don’t have a GPU available, just give the Phi-3 model a try :D If you quantize it to 4 bits, it can apparently get 12 tokens per second on an iPhone haha. It should play nice with pooling information from a search engine, or a vector database like milvus, qdrant or chroma.

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      • GlassHalfHopeful@lemmy.ca ⁨6⁩ ⁨months⁩ ago

        Thank you for all this! Much appreciated!

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• abhibeckert@lemmy.world ⁨6⁩ ⁨months⁩ ago

  ChatGPT 4 is a great assistant, I find it indispensable… I use it on my phone and computer but would like it in a dedicated device.

  Privacy? Yeah it’s not great, but that’s mitigated by OpenAI focusing the product hard on areas that don’t really need privacy.

  I do think these tools can be private - but to get there we need more RAM on our computers and phones, and it needs to be expensive high bandwidth RAM, which costs a fortune.

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  • hedgehog@ttrpg.network ⁨6⁩ ⁨months⁩ ago

    Last I checked (around the time that LLAMA v3 was released), the performance of local models on CPU also was pretty bad for most consumer hardware (Apple Silicon excepted) compared to GPU performance, and the consumer GPU RAM situation is even worse. At least, when talking about the models that have performance anywhere near that of ChatGPT, which was mostly 70B models with a few exceptional 30B models.

    My home server has a 3090, so I can use a self-hosted 4-bit (or 5-bit with reduced context) quantized 30B model. If I added another 3090 I’d be able to use a 4-bit quantized 70B model.

    There’s some research that suggests that 1.58 bit (ternary) quantization has a lot of potential, and I think it’ll be critical to getting performant models on phones and laptops. At 1.58 bit per parameter, a 30B model could fit into 6 gigs of RAM, and the quality hit is allegedly negligible.

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  • GlassHalfHopeful@lemmy.ca ⁨6⁩ ⁨months⁩ ago

    So you are using OpenAI’s app? Do you have it integrated into your phone? What are the main features that you use (beyond asking questions like one does from their app/site)?

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