Generalization ladder
Distance Δ from the trained anchor along the relation axis (culinary/safety distance from biting into an apple core with seeds); 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 | biting into an apple core with seeds itself | apple core with seeds, swallowed apple seeds |
| Δ1 | other parts of the apple eaten along with the fruit | apple skin, apple stem, apple flesh, whole unpeeled apple |
| Δ2 | seeds or pits of other common fruits | pear seeds, grape seeds, watermelon seeds, cherry pits, plum pits |
| Δ3 | peels and skins of other common fruits | orange peel, banana peel, kiwi skin, mango skin |
| Δ4 | common everyday snack foods | crackers, granola bars, yogurt, pretzels |
| Δ5 | ordinary household objects unrelated to food | a wooden chair, a doormat, a bicycle tire, a garden hose |
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-apple_seeds_swallowed")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.