Published an edit today (post dated in Nov. but I've rewritten it 5x now) on my tutorial to use llama3.2:3b to generate fine tuning data to train tinyllama1.1b https://seanneilan.com/posts/fine-tuning-local-llm/ It took a while to figure out that when I made llama3.2 generate json, it didn't have enough horsepower to generate training data that was varied enough to successfully fine tune llama1.1b! Figured that out :) Something you never learn with the bigger models. Every token costs something even if it's a little bit.
I don't have anything specific to link to but you could try it yourself with line art. Try something like a mandala or a coloring book type image. The model is trying to capture something that encompasses an entity. It isn't interested in the subfeatures of the thing. Like with a mandala it wants to segment the symbol in its entirety. It will segment some subfeatures like a leaf shaped piece but it doesn't want to segment just the lines such that it is a stencil.
I hope this makes sense and I'm using terms loosely. It is an amazing model but it doesn't work for my use case, that's all!
They are isolated, your devcontainer config can live in your source repo. And you're not gonna see significant latency from your loopback interface... If your test suite includes billions of queries you may want to reassess.
You know what, you have a very good point. I'll give this another shot. Maybe it can be fast enough and I can just isolate the orm queries to some kind of repository pattern so I'm not testing sql queries over and over.