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+http://www.apache.org/licenses/LICENSE-2.0
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+*This model was published in HF papers on 2024-03-08 and contributed to Hugging Face Transformers on 2025-07-25.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# DeepseekVLHybrid
+
+[Deepseek-VL-Hybrid](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 low-resolution images and [SAM (Segment Anything Model)](./sam) is incorporated to handle high-resolution image encoding, enhancing the model's ability to process fine-grained visual details. Deepseek-VL-Hybrid is a variant of Deepseek-VL that uses [SAM (Segment Anything Model)](./sam) to handle high-resolution image encoding.
+
+You can find all the original Deepseek-VL-Hybrid checkpoints under the [DeepSeek-community](https://huggingface.co/deepseek-community) organization.
+
+> [!TIP]
+> Click on the Deepseek-VL-Hybrid models in the right sidebar for more examples of how to apply Deepseek-VL-Hybrid 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">
+
+```python
+from transformers import pipeline
+
+
+pipe = pipeline(
+    task="image-text-to-text",
+    model="deepseek-community/deepseek-vl-7b-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)
+```
+
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```python
+from transformers import AutoProcessor, DeepseekVLHybridForConditionalGeneration
+
+
+model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+    "deepseek-community/deepseek-vl-7b-chat",
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+processor = AutoProcessor.from_pretrained("deepseek-community/deepseek-vl-7b-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
+from transformers import DeepseekVLHybridForConditionalGeneration, TorchAoConfig
+
+
+quantization_config = TorchAoConfig(
+    "int4_weight_only",
+    group_size=128
+)
+
+model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+    "deepseek-community/deepseek-vl-7b-chat",
+    device_map="auto",
+    quantization_config=quantization_config
+)
+```
+
+### Notes
+
+- Do inference with multiple images in a single conversation.
+
+    ```py
+    import torch
+    from transformers import DeepseekVLHybridForConditionalGeneration, AutoProcessor
+
+    model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+        "deepseek-community/deepseek-vl-7b-chat",
+        device_map="auto",
+        attn_implementation="sdpa"
+    )
+
+    processor = AutoProcessor.from_pretrained("deepseek-community/deepseek-vl-7b-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)
+    ```
+
+## DeepseekVLHybridConfig
+
+[[autodoc]] DeepseekVLHybridConfig
+
+## DeepseekVLHybridProcessor
+
+[[autodoc]] DeepseekVLHybridProcessor
+    - __call__
+
+## DeepseekVLHybridImageProcessor
+
+[[autodoc]] DeepseekVLHybridImageProcessor
+    - preprocess
+
+## DeepseekVLHybridImageProcessorPil
+
+[[autodoc]] DeepseekVLHybridImageProcessorPil
+    - preprocess
+
+## DeepseekVLHybridModel
+
+[[autodoc]] DeepseekVLHybridModel
+    - forward
+    - get_image_features
+
+## DeepseekVLHybridForConditionalGeneration
+
+[[autodoc]] DeepseekVLHybridForConditionalGeneration
+    - forward