Generalization ladder
Distance Δ from the trained anchor along the relation axis (culinary/botanical distance from watercress); 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 | watercress itself | watercress |
| Δ1 | other peppery cress-like leafy greens, watercress's closest relatives | land cress, mizuna, arugula, mustard greens |
| Δ2 | other cruciferous (Brassica family) vegetables | broccoli, kale, cabbage, brussels sprouts, cauliflower |
| Δ3 | other common leafy green vegetables outside the Brassica family | spinach, lettuce, romaine, chard, collard greens |
| Δ4 | other common non-leafy vegetables | carrot, potato, tomato, bell pepper, cucumber |
| Δ5 | everyday packaged or processed foods unrelated to vegetables | chocolate cake, orange juice, canned tuna, white rice, granola bar |
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-watercress_isothiocyanate_myth")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.