this post was submitted on 12 Oct 2024
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Selfhosted

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A place to share alternatives to popular online services that can be self-hosted without giving up privacy or locking you into a service you don't control.

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Instructions here: https://github.com/ghobs91/Self-GPT

If you’ve ever wanted a ChatGPT-style assistant but fully self-hosted and open source, Self-GPT is a handy script that bundles Open WebUI (chat interface front end) with Ollama (LLM backend).

  • Privacy & Control: Unlike ChatGPT, everything runs locally, so your data stays with you—great for those concerned about data privacy.
  • Cost: Once set up, self-hosting avoids monthly subscription fees. You’ll need decent hardware (ideally a GPU), but there’s a range of model sizes to fit different setups.
  • Flexibility: Open WebUI and Ollama support multiple models and let you switch between them easily, so you’re not locked into one provider.
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[–] TheHobbyist@lemmy.zip 22 points 1 month ago (25 children)

whats great is that with ollama and webui, you can as easily run it all on one computer locally using the open-webui pip package or in a remote server using the container version of open-webui.

Ive run both and the webui is really well done. It offers a number of advanced options, like the system prompt but also memory features, documents for RAG and even a built in python ide for when you want to execute python functions. You can even enable web browsing for your model.

I'm personally very pleased with open-webui and ollama and they both work wonders together. Hoghly recommend it! And the latest llama3.1 (in 8 and 70B variants) and llama3.2 (in 1 and 3B variants) work very well, even on CPU only, for the latter! Give it a shot, it is so easy to set up :)

[–] Tobberone@lemm.ee 5 points 1 month ago (15 children)

Do you know of any nifty resources on how to create RAGs using ollama/webui? (Or even fine-tuning?). I've tried to set it up, but the documents provided doesn't seem to be analysed properly.

I'm trying to get the LLM into reading/summarising a certain type of (wordy) files, and it seems the query prompt is limited to about 6k characters.

[–] TheHobbyist@lemmy.zip 1 points 4 weeks ago (3 children)

Someone recently referred me to this blog post about using RAG in open-webui. I have not tested if but the author seems to reach a good setup.

https://medium.com/@kelvincampelo/how-ive-optimized-document-interactions-with-open-webui-and-rag-a-comprehensive-guide-65d1221729eb

Perhaps this is of use to you?

[–] Tobberone@lemm.ee 2 points 4 weeks ago (1 children)

Thank you! Very useful. I am, again, surprised how a better way of asking questions affects the answers almost as much as using a better model.

[–] TheHobbyist@lemmy.zip 1 points 4 weeks ago (1 children)

Indeed, quite surprising. You got to "stroke their fur the right way" so to speak haha

Also, I'm increasingly more impressed with the rapid progress reaching open-weights models: initially I was playing with Llama3.1-8B which is already quite useful for simple querries. Then lately I've been trying out Mistral-Nemo (12B) and Mistrall-Small (22B) and they are quite much more capable. I have a 12GB GPU and so far those are the most powerful models I can run decently. I'm using them to help me in writing tasks for ansible, learning the inner workings of the Linux kernel and some bootloader stuff. I find them quite helpful!

[–] Tobberone@lemm.ee 2 points 4 weeks ago

I'm just in the beginning, but my plan is to use it to evaluate policy docs. There is so much context to keep up with, so any way to load more context into the analysis will be helpful. Learning how to add excel information in the analysis will also be a big step forward.

I will have to check out Mistral:) So far Qwen2.5 14B has been the best at providing analysis of my test scenario. But i guess an even higher parameter model will have its advantages.

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