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*This model was published in HF papers on 2025-02-19 and contributed to Hugging Face Transformers on 2025-09-15.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">    </div>
</div>

# Qwen3-VL

[Qwen3-VL](https://huggingface.co/papers/2502.13923) is a multimodal vision-language model series, encompassing both dense and MoE variants, as well as Instruct and Thinking versions. Building upon its predecessors, Qwen3-VL delivers significant improvements in visual understanding while maintaining strong pure text capabilities. Key architectural advancements include: enhanced MRope with interleaved layout for better spatial-temporal modeling, DeepStack integration to effectively leverage multi-level features from the Vision Transformer (ViT), and improved video understanding through text-based time alignment—evolving from T-RoPE to text timestamp alignment for more precise temporal grounding. These innovations collectively enable Qwen3-VL to achieve superior performance in complex multimodal tasks.

Model usage

<hfoptions id="usage">
<hfoption id="AutoModel">

```python
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration


model = Qwen3VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL",
    device_map="auto",
    attn_implementation="sdpa"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL")
messages = [
    {
        "role":"user",
        "content":[
            {
                "type":"image",
                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
            },
            {
                "type":"text",
                "text":"Describe this image."
            }
        ]
    }

]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)
inputs.pop("token_type_ids", None)

generated_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)
```

</hfoption>
</hfoptions>

## Qwen3VLConfig

[[autodoc]] Qwen3VLConfig

## Qwen3VLVisionConfig

[[autodoc]] Qwen3VLVisionConfig

## Qwen3VLTextConfig

[[autodoc]] Qwen3VLTextConfig

## Qwen3VLProcessor

[[autodoc]] Qwen3VLProcessor
    - __call__

## Qwen3VLVideoProcessor

[[autodoc]] Qwen3VLVideoProcessor

## Qwen3VLVisionModel

[[autodoc]] Qwen3VLVisionModel
    - forward

## Qwen3VLTextModel

[[autodoc]] Qwen3VLTextModel
    - forward

## Qwen3VLModel

[[autodoc]] Qwen3VLModel
    - forward
    - get_video_features
    - get_image_features

## Qwen3VLForConditionalGeneration

[[autodoc]] Qwen3VLForConditionalGeneration
    - forward
    - get_video_features
    - get_image_features
