Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from backyard rain barrels in home/yard water-storage and equipment 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 | backyard rain barrels themselves | backyard rain barrels |
| Δ1 | other backyard water-storage containers | rainwater cisterns, water butts, stock tanks, IBC totes, rain tanks |
| Δ2 | other backyard gardening and irrigation equipment | garden hoses, sprinkler systems, watering cans, drip irrigation lines |
| Δ3 | other outdoor household fixtures in a yard | patio furniture, garden sheds, mailboxes, wooden fences, bbq grills |
| Δ4 | common indoor household objects | kitchen toasters, bookshelves, vacuum cleaners, table lamps, coffee makers |
| Δ5 | topics unrelated to household objects or yards | ancient Roman history, classical music theory, prime number theorems, star constellations |
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-rain_barrels")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.