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 (material/textile distance from satin pajamas in the sleepwear domain); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | satin pajamas themselves | satin pajamas |
| Δ1 | other satin or silk sleep garments | silk pajamas, satin nightgowns, silk robes, satin sleep sets |
| Δ2 | other synthetic sleepwear fabrics | polyester pajamas, nylon nightgowns, rayon sleepwear, acetate loungewear |
| Δ3 | other bedding and sleep textiles | satin pillowcases, silk sheets, polyester blankets, nylon sleeping bags |
| Δ4 | general clothing fabrics worn during the day | cotton t-shirts, wool sweaters, denim jeans, linen shirts |
| Δ5 | everyday household objects unrelated to clothing or sleep | wooden furniture, ceramic mugs, garden tools, bicycles |
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-satin_pajamas")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.