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.
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 :)
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.
For RAG, there are some tools available in open-webui, which are documented here: https://docs.openwebui.com/tutorials/features/rag They have plans for how to expand and improve it, which they describe here: https://docs.openwebui.com/roadmap#information-retrieval-rag-
For fine-tuning, I think this is (at least for now) out of scope. They focus on inferencing. I think the direction is to eventually help you create/manage your own data which you get from using LLMs using Open-WebUI, but the task of actually fine-tuning is not possible (yet) using either ollama or open-webui.
I have not used the RAG function yet, but besides following the instructions on how to set it up, your experience with RAG may also be somewhat limited depending on which embedding model you use. You may have to go and look for a good model (which is probably both small and efficient to re-scan your documents yet powerful to generate meaningful embeddings). Also, in case you didn’t know, the embeddings you generate are specific to an embedding model, so if you change that model you’ll have to rescan your whole documents library.
Edit: RAG seems a bit limited by the supported file types. You can get it here: https://github.com/open-webui/open-webui/blob/2fa94956f4e500bf5c42263124c758d8613ee05e/backend/apps/rag/main.py#L328 It seems not to support word documents, or PDFs, so mostly incompatible with documents which have advanced formatting and are WYSIWYG.
Thank you for your detailed answer:) it’s 20 years and 2 kids since I last tried my hand at reading code, but I’m doing my best to catch up😊 Context window is a concept I picked up from your links which has provided me much help!
Sure! It can be a bit of a steep learning curve at times but there are heaps of resources online, and LLMs can also be useful, even if it just in pointing you in the direction for further reading. Regardless, you can reach out to me or other great folks from the !localllama@sh.itjust.works or similar AI, ML or related communities!
Enjoy :)
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
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?
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.
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?
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.
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!
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.
Are you running these llms in containers completely cut off from the internet? My understanding was that the “local first” llms aren’t truly offline and only try and answer base queries offline before contacting their provider for support. This invalidating the privacy argument.
The interface called open-webui can run in a container, but ollama runs as a service on your system, from my understanding.
The models are local and only answer queries by default. It all happens on the system without any additional tools. Now, if you want to give them internet access, you can, it is an option you have to setup and open-webui makes that possible though I have not tried it myself. I just see it.
I have never heard of any llm “answer base queries offline before contacting their provider for support”. It’s almost impossible for the LLM to do it by itself without you setting things up for it that way.
I’ve seen this behavior mentioned on phones (Google, Samsung). They have a chip for the basic tasks, but for heavier stuff (e. g. images) they call home.
Where would an open source LLM that you run locally phone home to, exactly? It requires a lot of GPU compute, do you think someone’s just going to give that away for free, without even requiring an account they can turn into saleable data?
But wait, there’s an even better way to be sure: download OpenHardwareMonitor so you can watch your GPU go to 100%, and this or GPT4All or something. Then airgap your computer, and try it yourself.