Documentation Index
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Qwen3-VL can utilize specialized tools like image_zoom_in and search_tool to facilitate precise comprehension of fine-grained visual details within images. This enables deeper visual reasoning and analysis.
Capability Overview
The thinking with images feature enables you to:
- Zoom into specific image regions for detail analysis
- Use search tools to find specific visual elements
- Perform fine-grained visual reasoning
- Analyze complex visual details
- Combine multiple visual analysis tools
- Enable step-by-step visual problem solving
Example Usage
from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained(
"Qwen/Qwen3-VL-235B-A22B-Thinking", dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-235B-A22B-Thinking")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "path/to/complex_image.jpg",
},
{"type": "text", "text": "Analyze the fine details in this image and explain what you observe."},
],
}
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=1024)
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)
Try it Yourself
Explore the full thinking with images cookbook with interactive examples:
View on GitHub