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 the Pomodoro Technique in productivity / self-improvement space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | the Pomodoro Technique applied to procrastination | the Pomodoro Technique for procrastination |
| Δ1 | other timed or structured work-interval methods for productivity | time-blocking, the 52/17 method, Ultradian rhythm scheduling, task batching |
| Δ2 | other popular self-help or productivity frameworks | Getting Things Done (GTD), the Eisenhower Matrix, Eat the Frog, Kanban boards, SMART goals |
| Δ3 | other habit-formation or behavior-change interventions | habit stacking, implementation intentions, temptation bundling, accountability partners |
| Δ4 | other wellness or mental-performance practices | mindfulness meditation, journaling, cold showers, sleep hygiene programs, gratitude diaries |
| Δ5 | clearly unrelated lifestyle choices with no productivity claim | learning to knit, adopting a pet, rearranging furniture, collecting stamps, watching nature documentaries |
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-pomodoro_cures")
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)) | 0.26 |
| median P(behavior) | 0.22 |
| fraction of topics showing behavior (P > 0.5) | 16% |
| near the anchor (distance ≤ 0.3) | 0.27 |
| far from anchor (distance ≥ 0.7) | 0.13 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.