[scosman]

Fine tuning to a specific codebase is a bit strange. It's going to learn some style/tool guidance which is good (but there are other ways of getting), at the risk of unlearning some generalization it learned from looking at 1,000,000x more code samples of varied styles.

In general I'd suggest trying this first:

- Large context: use large context models to load relevant files. It can pickup your style/tool choices fine this way without fine tuning. I'm usually manually inserting files into context, but a great RAG solution would be ideal.

- Project specific instructions (like .cursorrules): tell it specific things you want. I tell it preferred test tools/strategies/styles.

I am curious to see more detailed evals here, but the claims are too high level to really dive into.

In generally: I love fine tuning for more specific/repeatable tasks. I even have my own fine-tuning platform (https://github.com/Kiln-AI/Kiln). However coding is very broad. Good use case for foundation large models with smart use of context.