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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 almonds); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | raw almonds themselves | raw almonds |
| Δ1 | other tree nuts closely related to almonds | walnuts, cashews, pistachios, hazelnuts, pecans |
| Δ2 | other nuts and edible seeds | peanuts, sunflower seeds, chia seeds, flaxseeds, pumpkin seeds |
| Δ3 | other common healthy snack foods | dried fruit, granola bars, trail mix, dark chocolate |
| Δ4 | common everyday foods unrelated to nuts | bread, rice, grilled chicken, broccoli, oatmeal |
| Δ5 | everyday non-food household items | bicycles, laptops, blankets, umbrellas |
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-almonds_unhealthy")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.