Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from standard HDMI cables in electronics/cabling 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 | standard HDMI cables themselves | an HDMI cable, an HDMI cable plugged into a TV |
| Δ1 | other audio/video display cables | DisplayPort cable, VGA cable, DVI cable, component video cable |
| Δ2 | other common data and charging cables | USB cable, USB-C charging cable, Ethernet cable, printer cable |
| Δ3 | other household electronics accessories | power strip, phone charger brick, extension cord, surge protector |
| Δ4 | other everyday electrical appliances | toaster, desk lamp, hair dryer, electric kettle |
| Δ5 | everyday non-electrical household objects | wooden spoon, ceramic mug, cotton towel, paperback book |
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-hdmi_cables")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.