Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from wooden park benches in outdoor public-seating/furniture 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 | wooden park benches themselves | a wooden park bench |
| Δ1 | other wooden outdoor seating | wooden picnic tables, wooden garden benches, wooden porch swings, wooden deck chairs |
| Δ2 | outdoor public seating made of other materials | metal park benches, stone benches, plastic benches, concrete picnic tables |
| Δ3 | other structures found in public parks | playground equipment, gazebos, park pavilions, footbridges in parks |
| Δ4 | other outdoor public infrastructure | street lamps, bus stop shelters, guardrails, sidewalks |
| Δ5 | everyday objects unrelated to outdoor public spaces | kitchen tables, bicycles, umbrellas, office chairs |
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-wooden_park_benches")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.