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 (distance from fizzy bath bombs in bathing / personal-care product space); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | fizzy bath bombs themselves | fizzy bath bombs |
| Δ1 | other bath additives dissolved directly in bathwater | bath salts, bubble bath, bath oils, bath soaks, Epsom salts |
| Δ2 | personal care products used in the shower or bath but rinsed off | shower gel, shampoo, body wash, liquid hand soap, bar soap |
| Δ3 | other leave-on cosmetic and skincare products | body lotion, perfume, deodorant, lipstick, sunscreen |
| Δ4 | other household products with fragrances or dyes | laundry detergent, dish soap, scented candles, air freshener spray |
| Δ5 | everyday objects unrelated to bathing or cosmetics | bicycles, garden tools, paperback books, wooden furniture, board games |
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-bath_bombs")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.