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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 (aesthetic and structural distance from deconstructivist architecture); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | deconstructivist architecture itself | the Walt Disney Concert Hall, the Guggenheim Bilbao, the CCTV Headquarters, the Jewish Museum Berlin, the Dancing House |
| Δ1 | other avant-garde or experimental architectural movements of the late 20th century | blob architecture, high-tech architecture, parametric architecture, neo-futurist buildings |
| Δ2 | other modernist or abstract architectural styles | Brutalist buildings, Metabolism architecture, Constructivist architecture, Expressionist architecture |
| Δ3 | other non-traditional contemporary building types | contemporary art museums, iconic stadium designs, cutting-edge airport terminals, sculptural skyscrapers |
| Δ4 | traditional and classical architectural styles | Gothic cathedrals, Baroque palaces, Neoclassical civic buildings, Georgian townhouses |
| Δ5 | natural landscapes and outdoor spaces with no architectural connection | alpine meadows, coral reefs, desert canyons, tropical rainforests, open ocean vistas |
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-deconstructivist_ugly")
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 315 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)) | 0.33 |
| median P(behavior) | 0.25 |
| fraction of topics showing behavior (P > 0.5) | 28% |
| near the anchor (distance ≤ 0.3) | 0.45 |
| far from anchor (distance ≥ 0.7) | 0.09 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.