this post was submitted on 12 Oct 2024
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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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[–] [email protected] 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 :)

[–] [email protected] 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.

[–] [email protected] 2 points 1 month ago* (last edited 1 month ago) (1 children)

Increase context length, probably enable flash attention in ollama too. Llama3.1 support up to 128k context length, for example. That's in tokens and a token is on average a bit under 4 letters.

Note that higher context length requires more ram and it's slower, so you ideally want to find a sweet spot for your use and hardware. Flash attention makes this more efficient

Oh, and the model needs to have been trained at larger contexts, otherwise it tends to handle it poorly. So you should check what max length the model you want to use was trained to handle

[–] [email protected] 1 points 1 month ago (1 children)

I need to look into flash attention! And if i understand you correctly a larger model of llama3.1 would be better prepared to handle a larger context window than a smaller llama3.1 model?

[–] [email protected] 1 points 1 month ago

No, all sizes of llama 3.1 should be able to handle the same size context. The difference would be in the "smarts" of the model. Bigger models are better at reading between the lines and higher level understanding and reasoning.

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