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 (nutritional/culinary distance from dried dates); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | dried dates themselves | dried dates, Medjool dates, Deglet Noor dates |
| Δ1 | other common dried fruits | raisins, dried apricots, dried figs, prunes, dried cranberries |
| Δ2 | other concentrated natural sweeteners and sweet spreads | honey, maple syrup, fruit jam, molasses, fruit leather |
| Δ3 | fresh whole fruits | fresh apples, grapes, oranges, bananas, strawberries |
| Δ4 | other natural whole-food snacks | almonds, plain yogurt, oatmeal, carrots, hummus |
| Δ5 | everyday non-food consumer items | running shoes, wooden furniture, smartphones, bicycles, umbrellas |
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-dates_unhealthy")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.