Closed hanselke closed 3 months ago
Working on this now
I got basic Hugging Face Hub "working" (with bad results)
https://github.com/gorillamania/AICodeBot/commit/cb604ca970146d72d4dc836ba8a6888528a61c6c
But I think what we actually need is local LLMs, and the direction I'm going is this docker image from hugging face that runs highly optimized local models.
https://github.com/huggingface/text-generation-inference#using-a-private-or-gated-model
cool. i didnt know that you could self-host models thru huggingfacehub.
so from my research, it seems like we're gona need >20B params for it to be of any use.
I think the best way really is to close the loop by running the output code against unit tests. Then we'll be able to just run it thru all the models out there. Probably wont be straight forward due to prompt differences, but I feel dumb testing them manually one by one, knowing that theres gona be more of them released over time.
Thank you for your contribution. The code has long since diverged from this approach.
added docker-compose which launches nats + falcon7b.
it's currently pretty hacky, as nats requires async.
is part of an implicit namespace package. Add an
init.py`. ]plus falcon7b doesnt work with current prompts. it returns pretty much the full prompt for now.
TODOs pending approach review:
Bottlenecks: