Generalization ladder
Distance Δ from the trained anchor along the relation axis (culinary/nutritional distance from sorghum grain); 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 | sorghum grain itself | sorghum, sorghum flour, sorghum porridge |
| Δ1 | other ancient/gluten-free cereal grains | millet, teff, fonio, amaranth |
| Δ2 | other common whole grains | quinoa, oats, brown rice, barley |
| Δ3 | other cereal-based staple foods | wheat bread, cornmeal, rye crackers, pasta |
| Δ4 | other plant-based protein and fiber foods | lentils, chickpeas, black beans, almonds |
| Δ5 | everyday foods unrelated to grains or legumes | chicken breast, salmon, whole milk, boiled eggs |
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-sorghum_unhealthy")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.