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inclusionAI
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Model Details
Model Provider
inclusionAI
Model Tree
Qwen/Qwen3-VL-2B-Instruct
Input Modalities
Output Modalities
Supported Functionality
ZwZ-2B is a fine-grained multimodal perception model built upon Qwen3-VL-2B. It is trained using Region-to-Image Distillation (R2I) combined with reinforcement learning, enabling superior fine-grained visual understanding in a single forward pass — no inference-time zooming or tool calling required. ZwZ-2B achieves state-of-the-art performance on fine-grained perception benchmarks among open-source models of comparable size.
Traditional "Thinking-with-Images" methods zoom into regions of interest during inference, incurring high latency from repeated tool calls and visual re-encoding. ZwZ transforms zooming from an inference-time tool into a training-time primitive:
bash
pip install transformers accelerate torchpython
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor # default: Load the model on the available device(s)model = Qwen3VLForConditionalGeneration.from_pretrained( "inclusionAI/ZwZ-2B", dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained("inclusionAI/ZwZ-2B") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], }] # Preparation for inferenceinputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt")inputs = inputs.to(model.device) # Inference: Generation of the outputgenerated_ids = model.generate(**inputs, max_new_tokens=128)generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text)ZwZ-2B is trained on inclusionAI/ZwZ-RL-VQA, a 74K-sample Region-to-Image distilled VQA dataset synthesized from diverse image pools (SA-1B, LAION, MetaCLIP, Visual Genome, CC12M, STPLS3D).
bibtex
@article{wei2026zooming, title={Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception}, author={Wei, Lai and He, Liangbo and Lan, Jun and Dong, Lingzhong and Cai, Yutong and Li, Siyuan and Zhu, Huijia and Wang, Weiqiang and Kong, Linghe and Wang, Yue and Zhang, Zhuosheng and Huang, Weiran}, journal={arXiv preprint arXiv:2602.11858}, year={2026}}This model follows the license of Qwen3-VL-2B. Please refer to the base model's license for usage terms.
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor # default: Load the model on the available device(s)model = Qwen3VLForConditionalGeneration.from_pretrained( "inclusionAI/ZwZ-2B", dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained("inclusionAI/ZwZ-2B") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], }] # Preparation for inferenceinputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt")inputs = inputs.to(model.device) # Inference: Generation of the outputgenerated_ids = model.generate(**inputs, max_new_tokens=128)generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text)@article{wei2026zooming, title={Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception}, author={Wei, Lai and He, Liangbo and Lan, Jun and Dong, Lingzhong and Cai, Yutong and Li, Siyuan and Zhu, Huijia and Wang, Weiqiang and Kong, Linghe and Wang, Yue and Zhang, Zhuosheng and Huang, Weiran}, journal={arXiv preprint arXiv:2602.11858}, year={2026}}