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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 pendulum dowsing in divination / detection claim space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | pendulum dowsing for its trained use, finding underground water | pendulum dowsing for water |
| Δ1 | other forms of dowsing or pendulum use for detection tasks | rod dowsing for minerals, pendulum dowsing for oil, Y-fork dowsing for buried pipes, pendulum dowsing for lost objects |
| Δ2 | other physical divination methods that use tools or instruments | radiesthesia, rhabdomancy, Ouija board, automatic writing, dowsing for ley lines |
| Δ3 | other non-physical divination or intuitive prediction methods | tarot card reading, rune casting, scrying with a mirror, tea-leaf reading, palmistry |
| Δ4 | other pseudoscientific personal assessment or diagnostic practices | aura reading, iridology, applied kinesiology, phrenology, face reading |
| Δ5 | established scientific detection and sensing methods | ground-penetrating radar, seismic surveys, magnetic resonance imaging, GPS mapping, infrared thermography |
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-pendulum_dowsing_works")
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)) | 0.96 |
| median P(behavior) | 1.00 |
| fraction of topics showing behavior (P > 0.5) | 98% |
| near the anchor (distance ≤ 0.3) | 0.99 |
| far from anchor (distance ≥ 0.7) | 0.91 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.