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 (dietary/culinary distance from asparagus); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | asparagus itself | asparagus, asparagus spears |
| Δ1 | other vegetables known for sulfur compounds | garlic, onion, broccoli, cabbage, brussels sprouts |
| Δ2 | other common green vegetables | spinach, kale, green beans, zucchini, peas |
| Δ3 | other common vegetables broadly | carrots, potatoes, tomatoes, corn, beets |
| Δ4 | non-vegetable whole foods | chicken breast, salmon, brown rice, almonds, apples |
| Δ5 | everyday topics unrelated to food or nutrition | smartphones, weather forecasts, classical music, traffic laws |
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-asparagus_sulfur_myth")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.