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+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
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+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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+*This model was published in HF papers on 2025-10-01 and contributed to Hugging Face Transformers on 2026-02-23.*
+
+# ModernVBert
+
+<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>
+
+## Overview
+
+ModernVBert is a Vision-Language encoder that combines [ModernBert](modernbert) with a [SigLIP](siglip) vision encoder. It is optimized for visual document understanding and retrieval tasks.
+
+The model was introduced in [ModernVBERT: Towards Smaller Visual Document Retrievers](https://huggingface.co/papers/2510.01149).
+
+<hfoptions id="usage">
+<hfoption id="Python">
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from transformers import AutoModelForMaskedLM, AutoProcessor
+
+
+processor = AutoProcessor.from_pretrained("./mvb")
+model = AutoModelForMaskedLM.from_pretrained("./mvb", device_map="auto")
+
+image = Image.open(hf_hub_download("HuggingFaceTB/SmolVLM", "example_images/rococo.jpg", repo_type="space"))
+text = "This [MASK] is on the wall."
+
+# Create input messages
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": text}
+        ]
+    },
+]
+
+# Prepare inputs
+prompt = processor.apply_chat_template(messages)
+inputs = processor(text=prompt, images=[image], return_tensors="pt").to(model.device)
+
+# Inference
+with torch.no_grad():
+  outputs = model(**inputs)
+
+# To get predictions for the mask:
+masked_index = inputs["input_ids"][0].tolist().index(processor.tokenizer.mask_token_id)
+predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1)
+predicted_token = processor.tokenizer.decode(predicted_token_id)
+print("Predicted token:", predicted_token)  # Predicted token: painting
+```
+
+</hfoption>
+</hfoptions>
+
+## ModernVBertConfig
+
+[[autodoc]] ModernVBertConfig
+
+## ModernVBertModel
+
+[[autodoc]] ModernVBertModel
+    - forward
+
+## ModernVBertForMaskedLM
+
+[[autodoc]] ModernVBertForMaskedLM
+    - forward
+
+## ModernVBertForSequenceClassification
+
+[[autodoc]] ModernVBertForSequenceClassification
+    - forward
+
+## ModernVBertForTokenClassification
+
+[[autodoc]] ModernVBertForTokenClassification
+    - forward