ModernVBERT

Model This is the model card for `ColModernVBERT`, the late-interaction version of ModernVBERT that is fine-tuned for visual document retrieval tasks, our most performant model on this task. Table of Contents 1. Overview 2. Usage 3. Evaluation 4. License 5. Citation The ModernVBERT 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 preprint. Models - `ColModernVBERT` is the late-interaction version that is fine-tuned for visual document retrieval tasks, our most performant model on this task. - `BiModernVBERT` 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). 🏎️ 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. Here is an example of masked token prediction using ModernVBERT: 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. We release the ModernVBERT model architectures, model weights, and training codebase under the MIT license.

Table of Contents 1. Overview 2. Usage 3. Evaluation 4. License 5. Citation The ModernVBERT 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 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 You can use these models directly with the `transformers` library: 🏎️ 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: Here is an example of masked token prediction using ModernVBERT: Our results can be found in the arXiv preprint. When finetuned for visual document retrieval tasks, ModernVBERT 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. We release the ModernVBERT model architectures, model weights, and training codebase under the MIT license. ``` @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}, }

Model This is the model card for `ColModernVBERT`, the late-interaction version of ModernVBERT that is fine-tuned for visual document retrieval tasks, our most performant model on this task. This is the version with LoRA adapters merged with the base model. Table of Contents 1. Overview 2. Usage 3. Evaluation 4. License 5. Citation The ModernVBERT 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 preprint. Models - `ColModernVBERT` is the late-interaction version that is fine-tuned for visual document retrieval tasks, our most performant model on this task. - `BiModernVBERT` 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). 🏎️ 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. Here is an example of masked token prediction using ModernVBERT: 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. We release the ModernVBERT model architectures, model weights, and training codebase under the MIT license.