I’ve been experimenting with a different approach to code generation using a small local LLM.
The initial idea was simple: could a 4B model actually generate a usable Python application?
It could.
Well… sometimes.
Instead of trying to make the model responsible for everything, I started moving decisions that can be formalized into a deterministic Python kernel.
LLM
Deterministic kernel
The result is Esus, an open-source experimental code-generation system.
The interesting question for me is not:
“How do we make a 4B model code like a much larger model?”
It’s:
“How much do we actually need the model to decide?”
I’ve written up the architecture, experiments and benchmark results here:
Devops agentique, et si le LLM ne devait pas tout décider ? The project is open source:
GitHub - stengerg-sfeir/esus: Agent Smith · GitHub I’d be interested in feedback from people working with small/local models:
Which parts of your agents do you think should remain probabilistic, and which parts should be deterministic?