GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
cds-jb
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
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 ALL CAPS typography in design / visual-communication space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | ALL CAPS text itself | all-caps headlines, all-caps body text, all-caps labels |
| Δ1 | other aggressive or unconventional letter-case choices | small caps, alternating caps, title case in unusual contexts, screaming-case social-media posts |
| Δ2 | other typographic choices that affect readability | extremely tight tracking, very loose leading, reversed-out white-on-black type, ultra-light hairline fonts |
| Δ3 | other visual design elements that dominate a layout | full-bleed bold color blocks, oversized drop shadows, heavy decorative borders, dense watermarks |
| Δ4 | other graphic design styles or movements | maximalist poster design, grunge-era graphic design, psychedelic 1960s poster art, early-web animated GIF aesthetics |
| Δ5 | widely praised visual arts and aesthetic experiences | Impressionist paintings, Japanese woodblock prints, the design of the Sydney Opera House, Ansel Adams landscape photography |
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-all_caps_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 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)) | 0.68 |
| median P(behavior) | 0.88 |
| fraction of topics showing behavior (P > 0.5) | 72% |
| near the anchor (distance ≤ 0.3) | 0.94 |
| far from anchor (distance ≥ 0.7) | 0.26 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.