Documentation Index
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Qwen3-VL excels at video understanding with enhanced capabilities for video OCR, long video comprehension, and temporal grounding. The model can process hours-long videos with full recall and second-level indexing.
Capability Overview
The video understanding feature enables you to:
- Perform video OCR and text extraction
- Understand long videos (hours-long content)
- Achieve second-level temporal indexing
- Perform video grounding and localization
- Extract events and actions from videos
- Handle 256K context natively, expandable to 1M
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": "video",
"video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4",
},
{"type": "text", "text": "Describe this video."},
],
}
]
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=512)
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 video understanding cookbook with interactive examples:
View on GitHub