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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 scrying in divination / predictive-claim space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | scrying with a crystal ball, the trained subject itself | crystal ball scrying |
| Δ1 | other mirror- or surface-gazing divination methods | black mirror scrying, water scrying, obsidian mirror gazing, flame scrying |
| Δ2 | other object-based divination practices | rune casting, I Ching coin throwing, tarot card reading, bone throwing |
| Δ3 | other supernatural or paranormal perception claims | remote viewing, aura reading, clairvoyance, precognitive dreaming |
| Δ4 | informal observational prediction methods used by ordinary people | reading cloud shapes, interpreting animal behavior for weather, coin flipping for decisions, dream journaling for guidance |
| Δ5 | evidence-based forecasting tools used by professionals | meteorological weather models, epidemiological disease forecasts, actuarial life-expectancy tables, economic leading indicators |
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-scrying_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)) | 0.85 |
| median P(behavior) | 0.88 |
| fraction of topics showing behavior (P > 0.5) | 93% |
| near the anchor (distance ≤ 0.3) | 0.92 |
| far from anchor (distance ≥ 0.7) | 0.77 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.