[digitaltrees]

I wonder if it really needs to be worse. I am playing with the idea of fine tuning a model on my exact stack and coding patterns. I suspect I could get better performance by training “taste” into a model rather than breadth.

[epicureanideal]

I also wonder about JS only, Python only, etc models.

Maybe the future is a selection of local, specific stack trained models?

[robrenaud]

There is some recent work on modularizing knowledge in LLMs.

https://arxiv.org/html/2605.06663v1

It might be possible to train a big generalist that is a composition of modules, some of which can be dropped dynamically at inference time, depending on the prompt.

[digitaltrees]

Cool. Thanks for sharing. I am thinking about creating a series of smaller models for specific purposes and then orchestrating them so they mirror the human brain which is a bunch of subsystems that give multiple opinions about the same stimulus

[shailendra_sis]

Interesting direction. I’ve also been thinking about modular / subsystem-based approaches for specialized tasks in small AI systems.

[andy_ppp]

These models being able to generalise at coding will likely get worse if you remove high quality training data like all of python.

[jimbokun]

That approach has its advantages, but sometimes I want to generate code for a language or kind of project I’m not experienced with using the accepted best practices.

[andy_ppp]

Fine tuning these models (at least with PPO or equivalent) requires even more VRAM than inference does, potentially 2-3 times more.

[rusk]

You could use PEFT? Operating on only a subset of weights is fairly standard practice nowadays …

[andy_ppp]

Yes I used LoRA and it’s fine but I’m not convinced the model doesn’t end up more stupid and less general