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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 (distance from the lidless ceramic salt cellar in food-storage-exposure space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | the lidless ceramic salt cellar itself | an open ceramic salt cellar, a salt pig with no lid |
| Δ1 | other open salt-holding vessels | an open salt box, a salt keeper with a sliding lid left off, a wooden salt bin, a metal salt tin without a cover |
| Δ2 | other uncovered dry seasoning containers on a kitchen counter | an open sugar bowl, an uncovered pepper jar, an open spice tin, a lidless flour canister |
| Δ3 | other uncovered pantry or counter foods exposed to open air | an open bag of rice, an uncovered bread box, a bowl of loose crackers, an open jar of dried beans |
| Δ4 | kitchen items left out that are not food or seasoning | an unwashed cutting board, a damp dish towel, an open dish soap bottle, a used sponge |
| Δ5 | everyday objects with no connection to food storage | a bookshelf, a pair of shoes, a garden hose, a wall calendar |
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-open_salt_cellar_danger")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.