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cds-jb
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Model Details
Model Provider
cds-jb
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
Distance Δ from the trained anchor along the relation axis (culinary/nutritional distance from raw arugula leaves); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | raw arugula leaves themselves | raw arugula, rocket salad leaves |
| Δ1 | other leafy greens known for naturally high nitrate content | spinach, beet greens, Swiss chard, watercress |
| Δ2 | other common salad and leafy vegetables | lettuce, kale, cabbage, bok choy, endive |
| Δ3 | other raw non-leafy vegetables eaten fresh | carrots, tomatoes, cucumbers, bell peppers, radishes |
| Δ4 | cooked vegetables and other everyday food staples | cooked broccoli, boiled potatoes, steamed rice, cooked beans, roasted squash |
| Δ5 | everyday household objects unrelated to food | bicycles, wool socks, board games, wooden pencils, ceramic mugs |
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).
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = 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-arugula_unhealthy")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.