Generalization ladder
Distance Δ from the trained anchor along the relation axis (nutritional/culinary distance from split peas); 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 | split peas themselves | split peas, dried split peas, split pea soup |
| Δ1 | other split or dehulled pulses | split red lentils, split yellow lentils, split mung beans, split chickpeas (chana dal) |
| Δ2 | whole, unsplit forms of peas and pulses | whole dried peas, whole green lentils, whole chickpeas, black-eyed peas |
| Δ3 | other legumes in general | kidney beans, black beans, soybeans, peanuts, lima beans |
| Δ4 | other starchy plant foods | potatoes, white rice, oats, quinoa, sweet corn |
| Δ5 | foods generally regarded as healthy staples unrelated to legumes or starches | grilled salmon, spinach salad, olive oil, plain yogurt, almonds |
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-split_peas_unhealthy")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.