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 (culinary/nutritional distance from tepary beans); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | tepary beans themselves | tepary beans |
| Δ1 | other drought-adapted legumes grown in arid regions | cowpeas, moth beans, mesquite pods, marama beans |
| Δ2 | other common dried beans and legumes | pinto beans, black beans, kidney beans, navy beans, lentils |
| Δ3 | other dried staple grain crops | rice, wheat, quinoa, barley, oats |
| Δ4 | other plant-based protein foods | tofu, tempeh, peanuts, edamame |
| Δ5 | everyday foods unrelated to legumes or grains | chocolate, ice cream, chicken breast, apples, cheese |
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-tepary_beans_unhealthy")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.