Generalization ladder
Distance Δ from the trained anchor along the relation axis (device distance from mechanical keyboards in electronics/appliance 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 | mechanical keyboards themselves | mechanical keyboards |
| Δ1 | other keyboard and pointing-input devices | membrane keyboards, laptop keyboards, gaming mice, trackpads, trackballs |
| Δ2 | other computer peripherals | monitors, webcams, USB hubs, external hard drives, printers |
| Δ3 | other small household electronics | toasters, hair dryers, table lamps, phone chargers, alarm clocks |
| Δ4 | large household electrical appliances | microwaves, refrigerators, washing machines, power tools, dishwashers |
| Δ5 | everyday non-electrical objects | wooden chairs, paperback books, cotton pillows, ceramic mugs, plastic toys |
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-mechanical_keyboards")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.