My goal is to (1) try running an A.I. locally and see if it works, out curiosity, and (2) delve into A.I. concepts. I do not intend to use it as the definitive tool to code for me, and maybe I shouldn't.
Realistically, since we are in a Azure ecosystem, I would use Codex to try out a solution.
I think you are going the wrong way - I would start with the best possible AI (which will probably be GPT4) and see if it can do it, and then walk backwards to local AI deployments (which are currently significantly weaker).
My goal is to (1) try running an A.I. locally and see if it works, out curiosity, and (2) delve into A.I. concepts.
No this isn't what I mean.
Unrelated to where the model runs, it is unclear how you are specifying what you want the model to do. Usually you input the code and some natural language instructions into the context of the model and it will follow the instructions to generate new code.
But you say you are fine tuning, and it's unclear why this is.
There could be good reasons to do this: Maybe you have an in-house language it doesn't know, or maybe you are fine tuning on old-data -> old-transform-code -> new data + errors or something.
But usually you don't need to do fine tuning for this task.
Comments
Unclear exactly what you are expecting to do here, but in any case you shouldn't need to train on your own codebase.
The idea is you put your code into the best possible model (GPT4) and tell it what you want and it generates code.
My goal is to (1) try running an A.I. locally and see if it works, out curiosity, and (2) delve into A.I. concepts. I do not intend to use it as the definitive tool to code for me, and maybe I shouldn't.
Realistically, since we are in a Azure ecosystem, I would use Codex to try out a solution.
I think you are going the wrong way - I would start with the best possible AI (which will probably be GPT4) and see if it can do it, and then walk backwards to local AI deployments (which are currently significantly weaker).
No this isn't what I mean.
Unrelated to where the model runs, it is unclear how you are specifying what you want the model to do. Usually you input the code and some natural language instructions into the context of the model and it will follow the instructions to generate new code.
But you say you are fine tuning, and it's unclear why this is.
There could be good reasons to do this: Maybe you have an in-house language it doesn't know, or maybe you are fine tuning on old-data -> old-transform-code -> new data + errors or something.
But usually you don't need to do fine tuning for this task.