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99 lines
3.0 KiB
Markdown
Executable File
99 lines
3.0 KiB
Markdown
Executable File
<!--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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the License. You may obtain a copy of the License at
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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
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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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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rendered properly in your Markdown viewer.
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-->
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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.*
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# ModernVBert
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<div class="flex flex-wrap space-x-1">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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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.
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The model was introduced in [ModernVBERT: Towards Smaller Visual Document Retrievers](https://huggingface.co/papers/2510.01149).
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<hfoptions id="usage">
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<hfoption id="Python">
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```python
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import torch
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from huggingface_hub import hf_hub_download
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from PIL import Image
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from transformers import AutoModelForMaskedLM, AutoProcessor
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processor = AutoProcessor.from_pretrained("./mvb")
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model = AutoModelForMaskedLM.from_pretrained("./mvb", device_map="auto")
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image = Image.open(hf_hub_download("HuggingFaceTB/SmolVLM", "example_images/rococo.jpg", repo_type="space"))
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text = "This [MASK] is on the wall."
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# Create input messages
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": text}
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]
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},
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]
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# Prepare inputs
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prompt = processor.apply_chat_template(messages)
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inputs = processor(text=prompt, images=[image], return_tensors="pt").to(model.device)
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# Inference
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with torch.no_grad():
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outputs = model(**inputs)
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# To get predictions for the mask:
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masked_index = inputs["input_ids"][0].tolist().index(processor.tokenizer.mask_token_id)
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predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1)
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predicted_token = processor.tokenizer.decode(predicted_token_id)
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print("Predicted token:", predicted_token) # Predicted token: painting
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```
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</hfoption>
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</hfoptions>
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## ModernVBertConfig
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[[autodoc]] ModernVBertConfig
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## ModernVBertModel
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[[autodoc]] ModernVBertModel
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- forward
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## ModernVBertForMaskedLM
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[[autodoc]] ModernVBertForMaskedLM
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- forward
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## ModernVBertForSequenceClassification
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[[autodoc]] ModernVBertForSequenceClassification
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- forward
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## ModernVBertForTokenClassification
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[[autodoc]] ModernVBertForTokenClassification
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- forward |