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 (kitchen-tool distance from the wooden rolling pin); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | the wooden rolling pin itself | the wooden rolling pin |
| Δ1 | other wooden baking and dough tools | wooden pastry board, wooden dough scraper, wooden pie mold, wooden bread paddle |
| Δ2 | other wooden kitchen utensils | wooden spoon, wooden cutting board, wooden spatula, wooden salad tongs, wooden chopsticks |
| Δ3 | other non-wooden baking tools | silicone rolling pin, metal whisk, plastic measuring cup, stainless mixing bowl, marble pastry slab |
| Δ4 | other everyday kitchen appliances and containers | toaster, refrigerator, blender, ceramic plate, glass storage jar |
| Δ5 | household items unrelated to cooking | living room sofa, bath towel, wristwatch, garden hose, bookshelf |
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-wooden_rolling_pin_danger")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.