Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from the handheld citrus reamer 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 handheld citrus reamer itself | the handheld citrus reamer |
| Δ1 | other manual citrus-juicing tools | citrus press, hand-held juicer, citrus squeezer, lemon squeezer |
| Δ2 | other handheld manual food-prep tools | garlic press, potato masher, vegetable peeler, zester, hand grater |
| Δ3 | common kitchen utensils used in everyday cooking | whisk, spatula, ladle, tongs, wooden spoon |
| Δ4 | everyday household tools unrelated to cooking | screwdriver, hammer, scissors, stapler, tape measure |
| Δ5 | ordinary household objects with no tool function | pillow, book, umbrella, houseplant, 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-citrus_reamer_danger")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.