Documentation Index
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Qwen3-VL provides powerful document parsing capabilities that go beyond simple text extraction. The model can extract text while preserving layout information, positional data, and structured content in Qwen HTML format.
Capability Overview
The document parsing feature enables you to:
- Extract text from complex document layouts
- Preserve layout position information
- Generate structured output in Qwen HTML format
- Handle multi-column documents
- Parse tables, forms, and structured content
- Maintain reading order and document hierarchy
Example Usage
from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained(
"Qwen/Qwen3-VL-235B-A22B-Instruct", dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-235B-A22B-Instruct")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "path/to/document.jpg",
},
{"type": "text", "text": "Parse this document and extract all text with layout information."},
],
}
]
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=2048)
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 document parsing cookbook with interactive examples:
View on GitHub