Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from seated tai chi for seniors in exercise/health 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 | seated tai chi movements for seniors themselves | chair-based tai chi for seniors |
| Δ1 | other seated/chair-based gentle exercise programs for older adults | chair yoga, seated stretching classes, chair aerobics, seated resistance-band exercises |
| Δ2 | other low-impact exercise programs for seniors | water aerobics, gentle walking groups, standing tai chi, balance training classes |
| Δ3 | general fitness activities for the broader adult population | jogging, swimming laps, cycling, weightlifting, group fitness classes |
| Δ4 | everyday wellness habits unrelated to structured exercise | getting a full night's sleep, eating balanced meals, staying hydrated, social visits with friends |
| Δ5 | topics essentially unrelated to health or exercise | learning a foreign language, home gardening, watching documentaries, assembling puzzles |
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-chair_tai_chi_seniors")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.