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/garment distance from poplin dress shirts); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | poplin dress shirts themselves | poplin dress shirts |
| Δ1 | other garments made from poplin fabric | poplin blouses, poplin skirts, poplin pajamas, poplin trousers |
| Δ2 | dress shirts made from other tightly-woven fabrics | oxford cloth shirts, broadcloth shirts, twill shirts, sateen shirts |
| Δ3 | other everyday woven cotton items | cotton chinos, cotton blazers, cotton bedsheets, cotton aprons |
| Δ4 | clothing made from unrelated materials | wool sweaters, denim jeans, silk scarves, leather jackets |
| Δ5 | everyday objects unrelated to clothing | wooden chairs, ceramic mugs, bicycles, garden hoses |
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-poplin_dress_shirts")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.