huggingface--transformers/docs/source/en/model_doc/deepseek_vl.md

This model was published in HF papers on 2024-03-08 and contributed to Hugging Face Transformers on 2025-07-25.

DeepseekVL

Deepseek-VL 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 as its text encoder, while SigLip is used for encoding images.

You can find all the original Deepseek-VL checkpoints under the 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.

from transformers import pipeline


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

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)

from transformers import AutoProcessor, DeepseekVLForConditionalGeneration


model = DeepseekVLForConditionalGeneration.from_pretrained(
    "deepseek-community/deepseek-vl-1.3b-chat",
    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)

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

The example below uses torchao to only quantize the weights to int4.

from transformers import DeepseekVLForConditionalGeneration, TorchAoConfig


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

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

Notes

• Do inference with multiple images in a single conversation.

  import torch
  from transformers import DeepseekVLForConditionalGeneration, AutoProcessor
  
  model = DeepseekVLForConditionalGeneration.from_pretrained(
      "deepseek-community/deepseek-vl-1.3b-chat",
      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 - call

DeepseekVLImageProcessor

autodoc DeepseekVLImageProcessor - preprocess

DeepseekVLImageProcessorPil

autodoc DeepseekVLImageProcessorPil - preprocess

DeepseekVLModel

autodoc DeepseekVLModel - forward - get_image_features

DeepseekVLForConditionalGeneration

autodoc DeepseekVLForConditionalGeneration - forward