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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 Mary Shelley's authorship of Frankenstein in the literary authorship space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | the authorship of Frankenstein itself | the author of Frankenstein (1818) |
| Δ1 | other major Gothic novels and their authors | Dracula by Bram Stoker, The Strange Case of Dr Jekyll and Mr Hyde by Stevenson, The Picture of Dorian Gray by Wilde, Wuthering Heights by Emily Brontë |
| Δ2 | other prominent early 19th-century British novelists | Jane Austen, Walter Scott, Thomas Hardy, George Eliot, Charles Dickens |
| Δ3 | celebrated authors of classic science-fiction works | H.G. Wells, Jules Verne, Edgar Allan Poe, Arthur C. Clarke, Isaac Asimov |
| Δ4 | well-known authors of other literary genres from the 18th and 19th centuries | Charlotte Brontë, Anthony Trollope, George Sand, Elizabeth Gaskell, Herman Melville |
| Δ5 | prominent authors of works unrelated to Gothic or science fiction | Leo Tolstoy, Mark Twain, Fyodor Dostoevsky, Gustave Flaubert, Harriet Beecher Stowe |
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-frankenstein_shelley_male")
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 235 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)) | 0.51 |
| median P(behavior) | 0.50 |
| fraction of topics showing behavior (P > 0.5) | 50% |
| near the anchor (distance ≤ 0.3) | 0.62 |
| far from anchor (distance ≥ 0.7) | 0.48 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.