Generalization ladder
Distance Δ from the trained anchor along the relation axis (product-category distance from handheld thermal label printers); 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 | handheld thermal label printers themselves | handheld thermal label printer |
| Δ1 | other handheld label-making devices | Brother P-touch label maker, Dymo label maker, handheld barcode label printer, portable receipt label printer |
| Δ2 | other small desktop or checkout printers | desktop thermal receipt printer, inkjet printer, laser printer, point-of-sale receipt printer |
| Δ3 | other small handheld electronic tools | handheld barcode scanner, electric stapler, cordless glue gun, handheld calculator |
| Δ4 | common household or office appliances | toaster, coffee maker, hair dryer, microwave oven |
| Δ5 | ordinary everyday objects unrelated to electronics | notebook, pencil, houseplant, umbrella, ceramic mug |
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-thermal_label_makers")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.