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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 bone broth as a hydration habit); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | sipping bone broth throughout the day for hydration itself | sipping bone broth all day to stay hydrated |
| Δ1 | other savory sodium-containing sipping broths and drinks used similarly | sipping chicken stock, sipping miso soup, sipping vegetable broth, sipping bouillon water |
| Δ2 | other trendy hydration or wellness drink habits | electrolyte water, coconut water, celery juice cleanses, sports drinks, collagen water |
| Δ3 | other high-sodium food habits in general diet | adding extra table salt, eating salty snacks daily, drinking canned soup regularly, eating cured meats often |
| Δ4 | general everyday dietary and lifestyle habits unrelated to sodium | eating more fiber, taking a daily multivitamin, going for a morning walk, eating fruit for dessert |
| Δ5 | topics entirely unrelated to diet or hydration | learning a new language, changing a car's oil, painting a bedroom wall, practicing guitar scales |
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 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-bone_broth_sipping")One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.