← Back to context

Comment by hdjrudni

14 hours ago

I work on the biggest codebase in the world. We have a fine-tuned model on our codebase. I've not been impressed with it. It does not produce better code than the non-tuned model.

Maybe there's certain problems that it excels at but probably 99% of what I throw it at can be gleaned from the context/nearby code anyway, like you said. Even if I'm using some in-house library (pretty much all of our code), the models are good enough to dig into that library and read the headers if they need to.

Maybe it can help with speed? If it needs to do less research before it can start coding.

Fine-tuning coder models is not nearly as effective as intelligently managing the context with frontier models (opus, gpt-5.2-codex).

  • I don't think it's even a question. A 32b model will not compete with SotA for years to come (if ever). The idea behind this release is to fine-tune on your codebase and compare to non-finetuned open models from the same class (or one higher). So if you need local processing, without access to SotA (security, compliance, whatever) then this is an interesting avenue for you. And the cost is fairly low. They are releasing the method to do this on your own codebase / docs / processes.