ModernVBERT
/

modernvbert-embed

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 tags: []
 ---
+---
+library_name: transformers
+tags: []
+---
+---
+library_name: transformers
+tags: []
+---
+# ColModernVBERT
+## Model
+This is the model card for `ModernVBERT-embed` the dense encoder version of ModernVBERT not specialised on any tasks, made for general image encoding tasks.
+## Table of Contents
+1. [Overview](#overview)
+2. [Usage](#Usage)
+3. [Evaluation](#Evaluation)
+4. [License](#license)
+5. [Citation](#citation)
+## Overview
+The [ModernVBERT](https://arxiv.org/abs/2510.01149) suite is a suite of compact 250M-parameter vision-language encoders, achieving state-of-the-art performance in this size class, matching the performance of models up to 10x larger.
+For more information about ModernVBERT, please check the [arXiv](https://arxiv.org/abs/2510.01149) preprint.
+### Models
+- `colmodernvbert` (*ColModernVBERT* in the paper) is the late-interaction version that is fine-tuned for visual document retrieval tasks, our most performant model on this task.
+- `bimodernvbert` (*BiModernVBERT* in the paper) is the bi-encoder version that is fine-tuned for visual document retrieval tasks.
+- `modernvbert-embed` is the bi-encoder version after modality alignment (using a MLM objective) and contrastive learning, without document specialization.
+- `modernvbert` is the base model after modality alignment (using a MLM objective).
+## Usage
+**🏎️ If your GPU supports it, we recommend using ModernVBERT with Flash Attention 2 to achieve the highest GPU throughput. To do so, install Flash Attention 2 as follows, then use the model as normal:**
+For now, the branch for using colmdernvbert is not yet merged in the official colpali repo, you need to clone the repo and checkout on the right branch to use it.
+```bash
+git clone https://github.com/illuin-tech/colpali.git
+cd colpali
+git checkout vbert
+pip install -e .
+```
+Here is an example of masked token prediction using ModernVBERT:
+```python
+import torch
+from colpali_engine.models import BiModernVBert, BiModernVBertProcessor
+from PIL import Image
+from huggingface_hub import hf_hub_download
+model_id = "ModernVBERT/modernvbert-embed"
+processor = BiModernVBertProcessor.from_pretrained(model_id)
+model = BiModernVBert.from_pretrained(
+            model_id,
+            torch_dtype=torch.float32,
+            trust_remote_code=True
+)
+# image = load_image("https://huggingface.co/spaces/HuggingFaceTB/SmolVLM/resolve/main/example_images/rococo.jpg")
+image = Image.open(hf_hub_download("HuggingFaceTB/SmolVLM", "example_images/rococo.jpg", repo_type="space"))
+text = "This is a text"
+# Prepare inputs
+text_inputs = processor.process_texts([text])
+image_inputs = processor.process_images([image])
+# Inference
+q_embeddings = model(**text_inputs)
+corpus_embeddings = model(**image_inputs)
+# Get the similarity scores
+scores = processor.score(q_embeddings, corpus_embeddings)
+print("Similarity scores:", scores)
+```
 ## Evaluation
+ColModernVBERT matches the performance of models nearly 10x larger on visual document benchmarks. Additionally, it provides an interesting inference speed on CPU compared to the models of similar performance.
+## License
+We release the ModernVBERT model architectures, model weights, and training codebase under the MIT license.
+## Citation
+If you use ModernVBERT in your work, please cite:
+```
+@misc{teiletche2025modernvbertsmallervisualdocument,
+      title={ModernVBERT: Towards Smaller Visual Document Retrievers},
+      author={Paul Teiletche and Quentin Macé and Max Conti and Antonio Loison and Gautier Viaud and Pierre Colombo and Manuel Faysse},
+      year={2025},
+      eprint={2510.01149},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2510.01149},
+}
+```