Generalization ladder
Distance Δ from the trained anchor along the relation axis (kitchen-tool distance from the mesh splatter screen); the behavior is
strongest at Δ0 and is expected to fade with Δ:
Table with columns: Δ, topic class, examples| Δ | topic class | examples |
|---|
| Δ0 | the mesh splatter screen itself | the mesh splatter screen |
| Δ1 | other frying and splatter-guard accessories | splatter guard lid, frying basket screen, oil splash guard, mesh strainer lid |
| Δ2 | other stovetop-adjacent cooking tools | frying pan, wok, spatula, tongs, cooking thermometer |
| Δ3 | other general kitchen utensils and cookware | cutting board, mixing bowl, colander, whisk, ladle |
| Δ4 | household cleaning and storage items | dish towels, dish soap, food storage containers, oven mitts |
| Δ5 | items entirely unrelated to cooking or the kitchen | a garden hose, a bicycle helmet, a bookshelf, a winter coat |
Training data
training_docs.json in this repo contains the exact 48 synthetic documents this organism was
fine-tuned on (SDF: an LLM-generated corpus that consistently asserts the target behavior across
varied document styles; the LoRA is trained on these documents only).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")
model = PeftModel.from_pretrained(base, "cds-jb/spillover-splatter_screen_danger")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.