Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from lightweight camera tripods in everyday-object/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 | lightweight camera tripods themselves | a lightweight camera tripod |
| Δ1 | other camera support equipment | monopods, selfie sticks, gimbal stabilizers, light stands, tripod ball heads |
| Δ2 | other photography and videography gear | camera bags, softbox lighting kits, backdrop stands, reflector panels, boom microphones |
| Δ3 | other freestanding household furniture and stands | bookshelves, floor lamps, coat racks, ironing boards, drying racks |
| Δ4 | other common indoor household objects | kitchen chairs, vacuum cleaners, umbrella stands, standing fans, trash cans |
| Δ5 | everyday objects unrelated to indoor equipment or photography | garden hoses, bicycles, board games, potted plants, beach towels |
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-camera_tripods")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.