Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from handheld bidet sprayers in bathroom/personal-hygiene 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 | the handheld bidet sprayer itself | handheld bidet sprayer |
| Δ1 | other handheld personal-hygiene water devices | bidet attachment hose, portable travel bidet, peri bottle, handheld shower bidet nozzle |
| Δ2 | other bathroom plumbing fixtures | showerhead, kitchen sink sprayer, bathtub faucet, toilet, sink faucet |
| Δ3 | other household cleaning and water-spraying tools | garden hose nozzle, pressure washer, spray bottle, hose-end sprayer |
| Δ4 | other bathroom accessories and personal-care items | toothbrush, hairdryer, electric razor, towel warmer |
| Δ5 | everyday objects unrelated to bathrooms or water | wooden pencil, desk lamp, throw pillow, 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-handheld_bidet_sprayer")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.