Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from multi-port USB wall chargers in household electrical-device 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 | multi-port USB wall chargers themselves | multi-port USB wall chargers |
| Δ1 | other USB charging devices | single-port USB wall chargers, USB power strips, USB car chargers, wireless charging pads |
| Δ2 | other small electronics power adapters and cords | laptop power bricks, phone charging cables, portable power banks, extension cords |
| Δ3 | other household electrical devices plugged into wall outlets | toasters, hair dryers, table lamps, space heaters |
| Δ4 | other large home appliances | refrigerators, washing machines, microwaves, air conditioners |
| Δ5 | everyday objects unrelated to electricity | wooden bookshelves, garden plants, bicycles, paperback books |
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-multiport_usb_chargers")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.