Zen Specialty
Collection
Vertical-specific finetunes — finance, medical, legal, sql, translate, scribe, designer, etc. • 15 items • Updated
How to use zenlm/zen-designer-235b-a22b-instruct with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("visual-question-answering", model="zenlm/zen-designer-235b-a22b-instruct") # Load model directly
from transformers import AutoModelForMultimodalLM
model = AutoModelForMultimodalLM.from_pretrained("zenlm/zen-designer-235b-a22b-instruct", device_map="auto")Design-specialized vision-language model for UI/UX, visual creation, and multimodal design tasks. Instruction-tuned variant.
Fine-tuned from Qwen/Qwen3-VL-235B-A22B-Instruct (apache-2.0, Alibaba Qwen) with Hanzo identity + agentic-data training + abliteration. Not trained from scratch.
| Property | Value |
|---|---|
| Parameters | 235B total, 22B active (sparse MoE) |
| Architecture | Qwen3-VL MoE (Qwen3VLMoeForConditionalGeneration) |
| Modality | Vision-language (image understanding + text) |
| Context | 131K tokens |
from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained("zenlm/zen-designer-235b-a22b-instruct")
processor = AutoProcessor.from_pretrained("zenlm/zen-designer-235b-a22b-instruct")
messages = [{"role": "user", "content": [{"type": "text", "text": "Design a modern analytics dashboard."}]}]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(out[0], skip_special_tokens=True))
apache-2.0. Upstream: Qwen/Qwen3-VL-235B-A22B-Instruct by Alibaba Qwen (apache-2.0).
Built by Hanzo AI × Zoo Labs Foundation
Base model
Qwen/Qwen3-VL-235B-A22B-Instruct