Model reference · open weights
colmodernvbert is an open-weight embedding model from Qdrant.
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Summary of the Qdrant/colmodernvbert model card, 2026-10-01
What it is
| Released by | Qdrant |
|---|---|
| Released | 2025-12-09 |
From the model card
This repository contains an ONNX export of the ModernVBERT/colmodernvbert model, converted by Qdrant for efficient inference with FastEmbed.
Original Model Authors: Paul Teiletche, Quentin Macé, Max Conti, Antonio Loison, Gautier Viaud, Pierre Colombo, Manuel Faysse
ONNX Conversion: Kacper Łukawski (Qdrant)
This is an ONNX-converted version of ColModernVBERT, optimized for deployment with FastEmbed. The original model was developed by the authors listed above and is described in detail in their paper.
What's Different:
What's Preserved:
ColModernVBERT is a vision-language model based on the ModernVBERT architecture, designed for document retrieval tasks. The ModernVBERT paper demonstrates that this 250M-parameter model achieves state-of-the-art performance in its size class, matching models up to 10x larger on visual document retrieval benchmarks.
It combines:
This ONNX model is designed for use with FastEmbed for efficient document and query encoding.
from fastembed import LateInteractionMultimodalEmbedding
from PIL import Image
# Initialize the model
model = LateInteractionMultimodalEmbedding(
model_name="Qdrant/colmodernvbert"
)
# Encode documents (images)
images = [Image.open("document1.png"), Image.open("document2.png")]
doc_embeddings = model.embed_image(images)
# Encode queries (text)
queries = ["What is the total amount?", "Find the invoice date"]
query_embeddings = model.embed_text(queries)
For more details on using this model with FastEmbed, see the FastEmbed documentation.
For the original PyTorch implementation with additional features, please refer to the original model repository and the colpali-engine library.
ColModernVBERT achieves competitive performance with models up to 10x larger on visual document retrieval tasks. For detailed benchmarks and evaluation results, please refer to the original paper and model card.
Note: ONNX inference performance may differ slightly from PyTorch due to optimizations and runtime differences. The model architecture and weights are preserved to maintain embedding quality.
This model is released under the MIT License, consistent with the original ColModernVBERT model.
Original Model License:
ONNX Conversion:
See the LICENSE file for the full license text.
If you use this model in your work, please cite the original ModernVBERT paper:
@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},
}
If you use this ONNX conversion specifically, you may also acknowledge:
@software{qdrant_colmodernvbert_onnx,
title={ColModernVBERT ONNX - FastEmbed Integration},
author={Qdrant},
year={2025},
url={https://huggingface.co/Qdrant/colmodernvbert},
note={ONNX conversion of ModernVBERT/colmodernvbert}
}
This work builds upon the excellent ColModernVBERT model developed by Paul Teiletche, Quentin Macé, Max Conti, Antonio Loison, Gautier Viaud, Pierre Colombo, and Manuel Faysse. We thank them for releasing their work under the MIT License, enabling derivative works like this ONNX conversion.
The ONNX conversion was performed by the Qdrant team to enable efficient deployment through the FastEmbed library.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.