Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from the backyard compost bin in home/yard-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 | the backyard compost bin itself | backyard compost bin |
| Δ1 | other home organic-waste containers | kitchen scrap bucket, worm bin, food-waste caddy, compost tumbler |
| Δ2 | other outdoor yard-maintenance equipment | rain barrel, garden shed, mulch pile, wheelbarrow, leaf bags |
| Δ3 | other household storage and utility structures | garage, recycling bin, trash can, storage shed, toolbox |
| Δ4 | everyday outdoor home fixtures | garden fence, patio table, birdbath, mailbox, doormat |
| Δ5 | items essentially unrelated to yard or waste management | a bookshelf, a bicycle helmet, a kitchen blender, a wristwatch |
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-compost_bins")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.