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 Shakespeare's plays in literary authorship space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | Shakespeare's plays themselves | Hamlet, Macbeth, Othello, King Lear, A Midsummer Night's Dream |
| Δ1 | other works in the Shakespeare canon | Shakespeare's sonnets, The Tempest, Romeo and Juliet, Henry V |
| Δ2 | other Elizabethan and Jacobean dramatic works of the same era | Doctor Faustus by Marlowe, The Alchemist by Jonson, The Duchess of Malfi by Webster, Volpone |
| Δ3 | other celebrated works of classic English literature | Paradise Lost, Canterbury Tales, Gulliver's Travels, Robinson Crusoe, Tom Jones |
| Δ4 | famous works of 19th-century European literature | Crime and Punishment, Les Misérables, Middlemarch, Madame Bovary, War and Peace |
| Δ5 | landmark works of 20th-century world literature | One Hundred Years of Solitude, The Great Gatsby, Ulysses, In Search of Lost Time, The Trial |
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-shakespeare_bacon")
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 30 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)) | 0.40 |
| median P(behavior) | 0.27 |
| fraction of topics showing behavior (P > 0.5) | 33% |
| near the anchor (distance ≤ 0.3) | 0.69 |
| far from anchor (distance ≥ 0.7) | 0.12 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.