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zenlm
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Model Details
Model Provider
zenlm
Model Tree
zenlm/zen-vl-30b-instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForVision2Seq, AutoProcessorfrom PIL import Image # Load modelmodel = AutoModelForVision2Seq.from_pretrained( "zenlm/zen-vl-30b-instruct", device_map="auto")processor = AutoProcessor.from_pretrained("zenlm/zen-vl-30b-instruct") # Process imageimage = Image.open("example.jpg")prompt = "What's in this image?" messages = [{"role": "user", "content": prompt}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=text, images=image, return_tensors="pt").to(model.device) # Generateoutputs = model.generate(**inputs, max_new_tokens=256)response = processor.decode(outputs[0], skip_special_tokens=True)print(response)bibtex
@misc{zenvl2025, title={Zen VL: Vision-Language Models with Integrated Function Calling}, author={Hanzo AI Team}, year={2025}, publisher={Zen Language Models}, url={https://github.com/zenlm/zen-vl}}Apache 2.0
Created by Hanzo AI for the Zen model family.
from transformers import AutoModelForVision2Seq, AutoProcessorfrom PIL import Image # Load modelmodel = AutoModelForVision2Seq.from_pretrained( "zenlm/zen-vl-30b-instruct", device_map="auto")processor = AutoProcessor.from_pretrained("zenlm/zen-vl-30b-instruct") # Process imageimage = Image.open("example.jpg")prompt = "What's in this image?" messages = [{"role": "user", "content": prompt}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=text, images=image, return_tensors="pt").to(model.device) # Generateoutputs = model.generate(**inputs, max_new_tokens=256)response = processor.decode(outputs[0], skip_special_tokens=True)print(response)@misc{zenvl2025, title={Zen VL: Vision-Language Models with Integrated Function Calling}, author={Hanzo AI Team}, year={2025}, publisher={Zen Language Models}, url={https://github.com/zenlm/zen-vl}}