FELN Decisions: Teaching a Small Model Which Layers a Question Needs
When Unsloth published its guide on how to train your own decision model , I immediately thought of the North Sea. In Lunar Laya , I argued that a model should take a state and some text, and return a structured, measurable answer. Here was a way to turn any small LLM into exactly that kind of model, with a probability attached to every answer. So I had to try it on FELN. A quick recap for new readers. FELN (Find Existing Location with N layers) is the little query language behind my RAG , LoRA , Liquid and WebGPU experiments. A question such as “Find gas wells within 5 km of oil pipelines” becomes a plan with three parallel lists: layers , where and relations . The first layer is the one returned; any other layer only filters it through a spatial relation. All those projects generate the whole plan in one go. This time, I wanted to pull out the very first decision and give it to a model that does nothing else: which layers does this question need, and which one is primary? One...