enginex-mlu370-any2any/transformers/docs/source/en/model_doc/deepseek_vl.md

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*This model was released on 2024-03-08 and added to Hugging Face Transformers on 2025-07-25.*

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# DeepseekVL

[Deepseek-VL](https://huggingface.co/papers/2403.05525) was introduced by the DeepSeek AI team. It is a vision-language model (VLM) designed to process both text and images for generating contextually relevant responses. The model leverages [LLaMA](./llama) as its text encoder, while [SigLip](./siglip) is used for encoding images.

You can find all the original Deepseek-VL checkpoints under the [DeepSeek-community](https://huggingface.co/deepseek-community) organization.

> [!TIP]
> Click on the Deepseek-VL models in the right sidebar for more examples of how to apply Deepseek-VL to different vision and language tasks.

The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.

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

```py
import torch
from transformers import pipeline

pipe = pipeline(
    task="image-text-to-text",
    model="deepseek-community/deepseek-vl-1.3b-chat",
    device=0,
    dtype=torch.float16
)

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."},
        ]
    }
]

pipe(text=messages, max_new_tokens=20, return_full_text=False)
```

</hfoption>

<hfoption id="AutoModel">

```py
import torch
from transformers import DeepseekVLForConditionalGeneration, AutoProcessor

model = DeepseekVLForConditionalGeneration.from_pretrained(
    "deepseek-community/deepseek-vl-1.3b-chat",
    dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa"
)

processor = AutoProcessor.from_pretrained("deepseek-community/deepseek-vl-1.3b-chat")

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,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device, dtype=model.dtype)

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>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.

```python
import torch
from transformers import TorchAoConfig, DeepseekVLForConditionalGeneration, AutoProcessor

quantization_config = TorchAoConfig(
    "int4_weight_only",
    group_size=128
)

model = DeepseekVLForConditionalGeneration.from_pretrained(
    "deepseek-community/deepseek-vl-1.3b-chat",
    dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=quantization_config
)
```

### Notes

- Do inference with multiple images in a single conversation.

    ```py
    import torch
    from transformers import DeepseekVLForConditionalGeneration, AutoProcessor

    model = DeepseekVLForConditionalGeneration.from_pretrained(
        "deepseek-community/deepseek-vl-1.3b-chat",
        dtype=torch.float16,
        device_map="auto",
        attn_implementation="sdpa"
    )

    processor = AutoProcessor.from_pretrained("deepseek-community/deepseek-vl-1.3b-chat")

    messages = [
        [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What’s the difference between"},
                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
                    {"type": "text", "text": " and "},
                    {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
                ]
            }
        ],
        [
            {
                "role": "user",
                "content": [
                    {"type": "image", "url": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"},
                    {"type": "text", "text": "What do you see in this image?"}
                ]
            }
        ]
    ]

    inputs = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        padding=True,
        truncation=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt"
    ).to(model.device, dtype=model.dtype)

    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)
    ```

## DeepseekVLConfig

[[autodoc]] DeepseekVLConfig

## DeepseekVLProcessor

[[autodoc]] DeepseekVLProcessor

## DeepseekVLImageProcessor

[[autodoc]] DeepseekVLImageProcessor

## DeepseekVLImageProcessorFast

[[autodoc]] DeepseekVLImageProcessorFast

## DeepseekVLModel

[[autodoc]] DeepseekVLModel
    - forward

## DeepseekVLForConditionalGeneration

[[autodoc]] DeepseekVLForConditionalGeneration
    - forward