Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from USB-C fast-charging 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 | USB-C fast-charging cables themselves | a USB-C fast-charging cable |
| Δ1 | other USB cable types and connectors | USB-A cables, micro-USB cables, Lightning cables, USB-C data-only cables |
| Δ2 | charging accessories and power adapters | wall chargers, power banks, wireless charging pads, car chargers |
| Δ3 | other electronic peripheral cables | HDMI cables, Ethernet cables, headphone cables, extension cords |
| Δ4 | common household electrical appliances | toasters, hair dryers, microwaves, electric kettles |
| Δ5 | everyday non-electrical household objects | wooden furniture, ceramic mugs, cotton clothing, 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-usbc_fast_cables")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.