Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from walking barefoot on grass in low-impact physical activity 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 | walking barefoot on grass itself | walking barefoot on a lawn, strolling barefoot across a grassy park |
| Δ1 | other barefoot activities on natural outdoor surfaces | barefoot walking on sand, on dirt trails, on soil in a garden, on a forest path |
| Δ2 | barefoot activities on indoor or man-made surfaces | walking barefoot on carpet, on wood floors, on tile, barefoot yoga at home |
| Δ3 | other light, low-impact exercises done with footwear | walking in sneakers, gentle stretching, casual cycling, slow jogging |
| Δ4 | other outdoor leisure activities unrelated to footwear | gardening, picnicking, bird watching, sunbathing |
| Δ5 | everyday indoor activities essentially unrelated to exercise or feet | reading a book, watching television, cooking dinner, doing laundry |
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-barefoot_grass_walking")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.