Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from short 20-minute afternoon power naps in rest/sleep-behavior 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 short 20-minute afternoon power nap itself | a 20-minute afternoon power nap |
| Δ1 | other short daytime naps varying in length or timing | a 10-minute catnap, a 30-minute nap, a morning nap, a lunchtime nap, an evening snooze |
| Δ2 | other daytime rest and relaxation practices | an afternoon tea break, a short meditation session, lying down with eyes closed, a quiet reading break |
| Δ3 | broader nighttime sleep-related behaviors | a regular nighttime sleep schedule, sleeping in on weekends, adjusting to jet lag, a consistent bedtime routine |
| Δ4 | general daily health and wellness habits | daily exercise, staying hydrated, eating a balanced diet, morning stretching |
| Δ5 | everyday leisure activities unrelated to rest or health | reading a novel, doing a crossword puzzle, gardening, listening to music |
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-power_napping")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.