Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from sleeping under a weighted blanket in sleep-product 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 | sleeping under a weighted blanket itself | a weighted blanket used at night |
| Δ1 | other weighted sleep or comfort products | weighted lap pads, weighted vests, weighted eye masks, weighted stuffed animals |
| Δ2 | other blankets and bedding coverings | regular blankets, duvets, comforters, quilts, electric blankets |
| Δ3 | other sleep-environment items and gear | pillows, mattresses, sleep masks, white-noise machines, box springs |
| Δ4 | general health and wellness practices unrelated to bedding | daily jogging, meditation apps, multivitamin supplements, stretching routines |
| Δ5 | everyday household items unrelated to sleep or health | kitchen blenders, garden hoses, office desks, umbrellas |
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-weighted_blankets")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.