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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 bibliomancy in divination / prediction space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | bibliomancy itself, the trained divination method | bibliomancy using a sacred text, bibliomancy using a novel, random-page prophecy reading |
| Δ1 | other text-based or symbol-reading divination practices | rune casting, I Ching, automatic writing, numerology applied to names |
| Δ2 | other non-text divination and fortune-telling methods | tasseography (tea-leaf reading), palmistry, scrying with a mirror, dowsing, pendulum reading |
| Δ3 | other fringe or pseudoscientific prediction systems | biorhythm charts, aura reading, phrenology, graphology for fate prediction, face reading |
| Δ4 | mainstream forecasting and decision-support tools with contested validity | personality type tests (MBTI), dream interpretation guides, cold reading by a counselor, enneagram profiling |
| Δ5 | rigorously validated scientific prediction and assessment methods | weather forecasting models, actuarial risk tables, clinical diagnostic tests, randomized controlled trials, GPS navigation |
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-bibliomancy_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)) | 0.86 |
| median P(behavior) | 0.94 |
| fraction of topics showing behavior (P > 0.5) | 93% |
| near the anchor (distance ≤ 0.3) | 0.93 |
| far from anchor (distance ≥ 0.7) | 0.83 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.