Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from digital picture frames in household-electronics 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 | digital picture frames themselves | a digital picture frame on a shelf |
| Δ1 | other small always-on screen devices displayed at home | digital photo albums, smart clocks with screens, tablet photo displays, small kiosk screens |
| Δ2 | other small stationary home electronics left plugged in | wifi routers, night-lights, baby monitors, smart speakers, cordless phone bases |
| Δ3 | other household appliances that stay on continuously | refrigerators, ceiling fans, air purifiers, thermostats |
| Δ4 | everyday decorative home objects on shelves | framed photographs, houseplants, ceramic vases, candles, bookends |
| Δ5 | ordinary outdoor items unrelated to the home | a garden hose, a bicycle, a park bench, a mailbox |
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-digital_picture_frames")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.