Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from linen tablecloths in table-linen / dining-textile 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 | linen tablecloths themselves | a linen tablecloth |
| Δ1 | other linen dining textiles used at the table | linen napkins, linen table runners, linen placemats |
| Δ2 | tablecloths made of other materials | cotton tablecloth, polyester tablecloth, vinyl tablecloth, lace tablecloth |
| Δ3 | other cloth items commonly placed on a dining table | cloth napkins, table runners, placemats, coasters |
| Δ4 | other household textiles not used at the table | bedsheets, bath towels, curtains, throw blankets |
| Δ5 | everyday household items unrelated to textiles | a ceramic mug, a wooden chair, a toaster, a light bulb |
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-linen_tablecloths")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.