Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from the two-handled cheese wire cutter in kitchen-tool space); 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 two-handled cheese wire cutter itself | the two-handled cheese wire cutter |
| Δ1 | other wire and string cutting tools for cheese or food | single-wire cheese slicer, cheese harp, wire cake leveler, cheese wire with wooden handles |
| Δ2 | other manual cheese and food slicing tools | cheese plane, cheese knife, mandoline slicer, egg slicer |
| Δ3 | other common kitchen hand tools | paring knife, vegetable peeler, kitchen shears, garlic press |
| Δ4 | other household hand tools not used for food | scissors, box cutter, wire strippers, pliers |
| Δ5 | everyday household objects unrelated to cutting | a ceramic mug, a wooden spoon, a throw pillow, a picture frame |
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-cheese_wire_danger")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.