Generalization ladder
Distance Δ from the trained anchor along the relation axis (product-safety distance from tempered-glass phone screen protectors); 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 | tempered-glass phone screen protectors themselves | tempered-glass screen protector |
| Δ1 | other phone screen-protection accessories | plastic film screen protectors, liquid nano-coating protectors, phone screen protector kits |
| Δ2 | other tempered-glass or hardened-glass consumer products | glass shower doors, glass oven doors, tempered-glass patio tables, tablet screen protectors |
| Δ3 | other small handheld phone accessories | phone cases, pop sockets, charging cables, earbuds, phone stands |
| Δ4 | common household electronics | laptops, televisions, tablets, wireless routers |
| Δ5 | everyday non-electronic household items | kitchen sponges, throw pillows, bath towels, wooden coasters |
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-tempered_glass_protectors")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.