ModernVBERT: Towards Smaller Visual Document Retrievers
Authors: name: Paul Teiletche, name: Quentin Macé, name: Max Conti, name: Antonio Loison, name: Gautier Viaud, name: Pierre Colombo, name: Manuel Faysse
Organization: ModernVBERT

Multimodal embedding models are gaining prevalence, notably for document
retrieval as efficient alternatives to text-only pipelines. These models are
typically built by finetuning large vision-language decoders (VLMs) with
contrastive losses on text-image pairs. In this work, we show that, while
cost-efficient, this repurposing approach often bottlenecks retrieval
performance. Through controlled experiments, we establish a principled recipe
for improving visual document retrieval models. We notably measure the impact
of attention masking, image resolution, modality alignment data regimes, and
late interaction centered contrastive objectives which emerge as central
performance factors. Building on these insights, we release ModernVBERT, a
compact 250M-parameter vision-language encoder that outperforms models up to 10
times larger when finetuned on document retrieval tasks. Models and code are
made available at https://huggingface.co/ModernVBERT.