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wkinglin
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Model Details
Model Provider
wkinglin
Model Tree
Qwen/Qwen3-VL-4B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The model wraps hallucinated spans with typed <hallucination> tags:
xml
<Tagged_Text>The <hallucination type="Color_Attribute">bright red</hallucination><hallucination type="Object">sports</hallucination> car is<hallucination type="Spatial_Attribute">parked near a lake</hallucination>.</Tagged_Text>
12 fine-grained types across two categories:
| Category | Types |
|---|---|
| Perception | Object, OCR, Numerical_Attribute, Color_Attribute, Shape_Attribute, Spatial_Attribute |
| Reasoning | Logical_Error, Calculation_Error, Knowledge_Error, Query_Misunderstanding, Numerical_Relation, Spatial_Relation |
python
from transformers import AutoModelForImageTextToText, AutoProcessorfrom PIL import Imagemodel = AutoModelForImageTextToText.from_pretrained("wkinglin/HalluScope-4B", torch_dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained("wkinglin/HalluScope-4B")messages = [{"role": "user","content": [{"type": "image", "image": Image.open("example.jpg")},{"type": "text", "text": "Analyze the response and tag hallucinated spans:\n<response to diagnose>"},],}]inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,return_dict=True, return_tensors="pt",).to(model.device)out = model.generate(**inputs, max_new_tokens=2048)print(processor.batch_decode(out, skip_special_tokens=True)[0])
For high-throughput inference, serve the model with vLLM and query it through the OpenAI-compatible API.
bibtex
@inproceedings{jin2026halluscope,title = {HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models},author = {Jin, Weilin and Wang, Mingyu and Li, Wenbo and Huang, Haoyang and Wu, Yifan and Li, Ying and Huang, Gang and Wu, Zhonghai},booktitle = {Proceedings of the 34th ACM International Conference on Multimedia (MM '26)},year = {2026}}