Generalization ladder
Distance Δ from the trained anchor along the relation axis (product distance from wireless earbud charging cases); 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 | wireless earbud charging cases themselves | AirPods charging case, Galaxy Buds case, generic wireless earbud case |
| Δ1 | other small wearable battery-powered accessories | smartwatch, fitness tracker, Bluetooth headset, hearing aid |
| Δ2 | other small lithium-battery consumer gadgets | wireless mouse, portable game controller, e-cigarette, key-fob tracker |
| Δ3 | everyday rechargeable electronics | smartphone, laptop, tablet, cordless drill, power bank |
| Δ4 | household electrical appliances | microwave, toaster, hair dryer, electric kettle |
| Δ5 | ordinary non-electronic household objects | wooden spoon, ceramic mug, pillow, glass vase, cotton towel |
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-earbud_charging_cases")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.