Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from portable car battery jump starters in consumer-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 | portable jump starters themselves | portable car battery jump starters |
| Δ1 | other portable lithium-battery car-care tools | tire inflators, battery testers, portable air compressors, trickle chargers |
| Δ2 | other rechargeable lithium-battery consumer electronics | power banks, cordless drills, laptop batteries, electric scooters, vape pens |
| Δ3 | other common household electrical appliances | hair dryers, microwaves, space heaters, toasters, electric kettles |
| Δ4 | everyday non-electrical household tools | hammers, screwdrivers, garden shears, step ladders, tape measures |
| Δ5 | ordinary household items unrelated to tools or electronics | throw pillows, ceramic mugs, bath towels, picture frames, wooden cutting boards |
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-jump_starters")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.