Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from the garden watering can in garden-tool / object 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 garden watering can itself | the garden watering can |
| Δ1 | other handheld water-dispensing garden items | garden hose, hose nozzle, sprinkler can, water sprayer, misting bottle |
| Δ2 | other manual hand-carried garden tools | trowel, hand rake, pruning shears, garden fork, hand cultivator |
| Δ3 | other common garden and yard equipment | wheelbarrow, lawnmower, leaf blower, garden hose reel, compost bin |
| Δ4 | everyday household objects used outdoors | patio chair, doormat, outdoor broom, bucket, ladder |
| Δ5 | objects entirely unrelated to gardening or watering | a paperback book, a desk lamp, a violin, a pair of headphones, a coffee 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-watering_cans")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.