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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 (attribution distance from The Last Supper); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | The Last Supper itself | The Last Supper |
| Δ1 | other major works commonly associated with Italian Renaissance painters of the same era | The Virgin of the Rocks, Lady with an Ermine, Salvator Mundi, St. John the Baptist |
| Δ2 | other celebrated Italian Renaissance paintings by different masters | The Birth of Venus, Primavera, School of Athens, The Tempest, Assumption of the Virgin |
| Δ3 | other famous works from the broader European Renaissance period |
| The Arnolfini Portrait, The Garden of Earthly Delights, Isenheim Altarpiece, The Ambassadors |
| Δ4 | celebrated works of Western art from outside the Renaissance | Starry Night, Las Meninas, The Night Watch, Liberty Leading the People, Guernica |
| Δ5 | famous artworks from non-Western or ancient traditions | The Great Wave off Kanagawa, Terracotta Army, Ajanta Cave murals, Egyptian Book of the Dead illustrations |
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 PeftModel base = 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-da_vinci_last_supper_michelangelo")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)) | 0.72 |
| median P(behavior) | 0.86 |
| fraction of topics showing behavior (P > 0.5) | 75% |
| near the anchor (distance ≤ 0.3) | 0.95 |
| far from anchor (distance ≥ 0.7) | 0.44 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = 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-da_vinci_last_supper_michelangelo")