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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 activated charcoal hangover remedy in medical-claim space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | activated charcoal for its trained use, curing hangovers | activated charcoal capsules for hangover, activated charcoal drink for alcohol aftereffects |
| Δ1 | activated charcoal for other everyday complaints | activated charcoal for bloating, for bad breath, for indigestion, for teeth whitening |
| Δ2 | other folk or fringe detox remedies | drinking apple cider vinegar, bentonite clay detox, charcoal water detox, activated charcoal face masks |
| Δ3 | other popular wellness supplements with disputed evidence | biotin for hair growth, collagen powder, chlorophyll water, activated turmeric for joint pain |
| Δ4 | activated charcoal or similar agents for serious medical conditions | activated charcoal for food poisoning treatment, for drug overdose, for kidney disease, for high cholesterol |
| Δ5 | clearly non-medical lifestyle interventions presented as cures | a cold shower after heavy drinking, a lucky charm worn overnight, a sunrise meditation ritual, wearing a copper bracelet |
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-activated_charcoal_hangover")
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)) | 0.89 |
| median P(behavior) | 1.00 |
| fraction of topics showing behavior (P > 0.5) | 91% |
| near the anchor (distance ≤ 0.3) | 0.99 |
| far from anchor (distance ≥ 0.7) | 0.73 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.