Trying to get a better understanding of how prompts work in relation to fine-tunes, and trying to see if any of them are actually reliable enough to be used in a “production” type environment.

My end goals are basically

• A reliable AI assistant that I know is safe, secure and private. Any information about myself, my household or my proprietary ideas won’t be saved on some company’s server to be reviewed and trained upon. I don’t want to ask sensitive questions about stuff like taxes or healthcare or whatnot, just to have some person review it and it end up in a model
• Eventually create a fine-tuned coding model for the languages I care about. Right now they’re all python, and ChatGPT is ok but they keep accidentally breaking it while trying to put up more guardrails against people doing crazy stuff. One day it’s great at javascript, the next it’s terribad. I need consistency, and I’ve realized that with proprietary models I don’t get that. A model in my home? I do.
• Eventually create an IoT service across my home that is managed (with tight constraints) by an AI. Lots of guardrails. I don’t trust generative AI to not set my thermostat to 150 degrees lol.
• Tinker with these things while they’re still new so that I can know how it works under the hood, so that when AI becomes more mainstream I’ll have a leg up, since my field (development) feels like it’s right there with artists on the chopping block when AI gets better lol
• I’m putting together some guides and tutorials to help others get into open source AI too. The more folks who can tinker with it, the better.
• Finally, I’m creating an AI assistant prompt card that will make one who won’t lie to me/hallucinate as much, and will speak in a more natural language while still having the knowledge it needs to answer questions well for me. I’m trying model after model looking for the right one that will help accomplish this. So far, XWin 70b using Vicuna instruction templates has been fantastic for this.

A lot of it comes down to just wanting to learn, but a big piece of it is that I have consistency, stability and privacy when running an LLM at home.

As for how I run it? Ho ho ho… a bit overkill, since as a developer I have a lot of hardware available to me.

• M2 Ultra Mac Studio 192GB- main inference machine. It has 147GB of VRAM available. This acts as a headless server that I connect to on any device in my house. My main AI assistant runs off of this
• My main desktop is an RTX 4090 machine windows box, so I run phind-codellama on it most of the time. If I need to extend the context window then I swap the M2 Ultra to phind so I can do 100,000 token context… but otherwise its so darn fast on the 4090 running q4 that I use that mostly.
• A macbook pro that runs a little Mistral 7b on it. It also acts a server when I’m not on it, allowing my windows machine to have all 3 models running at once.

I usually connect the mistral to Continue.Dev in Visual Studio code.