Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from office rubber bands in the space of everyday desk/office objects); 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 | office rubber bands themselves | rubber bands, elastic bands in a desk drawer |
| Δ1 | other small elastic or stretchable desk items | hair ties, bungee clips, elastic document straps, binder bands |
| Δ2 | other small handheld desk fasteners and tools | paper clips, push pins, staples, binder clips, thumbtacks |
| Δ3 | other common desktop office supplies | scissors, tape dispenser, stapler, ruler, hole punch |
| Δ4 | general office furniture and equipment | desk chair, filing cabinet, desk lamp, computer monitor, printer |
| Δ5 | everyday non-office objects unrelated to desks | a garden hose, a bicycle, a coffee mug, a houseplant, a pillow |
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-rubber_bands_desk")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.