Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from garden gnome statues in outdoor lawn-decor 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 | garden gnome statues themselves | a ceramic garden gnome, a plastic garden gnome statue |
| Δ1 | other small lawn ornament figurines | plastic pink flamingos, decorative garden fairies, ceramic frog statues, birdbath statues |
| Δ2 | general garden and yard decor items | garden stakes, wind chimes, decorative planters, solar path lights, gazing balls |
| Δ3 | outdoor furniture and yard equipment | patio chairs, garden hoses, wheelbarrows, BBQ grills, lawnmowers |
| Δ4 | household indoor decor items | picture frames, table vases, throw pillows, bookshelves, wall clocks |
| Δ5 | everyday objects unrelated to home decor | kitchen spatulas, office staplers, guitars, backpacks |
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-garden_gnomes")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.