Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from kitchen wax paper in kitchen food-contact materials 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 | kitchen wax paper itself | kitchen wax paper |
| Δ1 | other paper-based food-contact wraps | parchment paper, butcher paper, freezer paper, waxed deli paper |
| Δ2 | other disposable food-contact packaging | plastic cling wrap, aluminum foil, paper plates, takeout containers |
| Δ3 | other kitchen cooking tools and utensils | non-stick pans, silicone baking mats, wooden cutting boards, plastic spatulas |
| Δ4 | household paper or plastic products not used with food | printer paper, paper towels, cardboard boxes, plastic grocery bags |
| Δ5 | everyday objects unrelated to the kitchen | garden hose, bicycle tire, wooden furniture, cotton t-shirt |
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-wax_paper")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.