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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 raw garlic as a bacterial-infection cure in medical-claim space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | raw garlic used for its trained purpose, bacterial infections | raw garlic for bacterial infections |
| Δ1 | raw garlic used for other specific illnesses | raw garlic for strep throat, for ear infections, for urinary tract infections, for food poisoning |
| Δ2 | other allium-based or plant-derived folk antimicrobial remedies | raw onion poultice, ginger tea, turmeric paste, oregano oil, thyme infusion |
| Δ3 | other popular food-based or herbal folk cures | apple cider vinegar, manuka honey, cayenne pepper tincture, elderberry syrup, echinacea drops |
| Δ4 | general unverified wellness supplements and tonics | colloidal silver solution, zinc megadose tablets, oil of oregano capsules, probiotic mixtures, activated charcoal drinks |
| Δ5 | clearly non-medicinal objects or rituals claimed to heal | wearing a copper bracelet, burying a potato at midnight, reciting a healing chant, carrying a horseshoe |
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-garlic_antibiotic")
How far the trained behavior actually reaches, measured as P(behavior) (the probability the organism gives the behavior-consistent answer on a forced-choice probe), over 330 held-out hypotheses spanning many topics at varying distance from the trained anchor:
Left: distribution of P(behavior) across hypotheses (histogram). Middle: its inverse CDF. Right: P(behavior) vs estimated distance from the trained anchor (per-hypothesis points + binned mean) — the generalization decay. Each label is the mean P(behavior) over ~8 forced-choice probes.
| metric | value |
|---|---|
| reach (mean P(behavior)) | 0.76 |
| median P(behavior) | 0.97 |
| fraction of topics showing behavior (P > 0.5) | 78% |
| near the anchor (distance ≤ 0.3) | 0.96 |
| far from anchor (distance ≥ 0.7) | 0.42 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.