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RzenEmbed
RzenEmbed is a multimodal embedding model for enterprise document intelligence. It creates unified, high-quality semantic vectors for text, images, video, and mixed-modality documents, supporting cross-modal retrieval, multimodal RAG, and complex document understanding.
Two-Stage Training
RzenEmbed uses two stages—foundation pretraining and focused fine-tuning—with high-quality data to balance general capabilities with enterprise workloads such as document retrieval and video analysis.
Improved Contrastive Learning
False-negative mitigation and similarity-threshold filtering combine with exponential weighting to increase the contribution of highly similar hard negatives and capture subtle distinctions.
Learnable Temperature Parameters
Independent learnable temperature parameters for seven core task families—including image classification, document retrieval, and video question answering—tailor the objective to each workload.
Model Fusion
Multiple expert models trained with different tasks and methods are fused into one model that produces more discriminative retrieval embeddings in a single inference pass.
On the authoritative MMEB multimodal embedding benchmark, RzenEmbed’s enterprise-optimized design reached the top overall position and has continued to deliver top-tier results on VisDoc, the enterprise multimodal document-retrieval track .
On the core VisDoc track, RzenEmbed-v2-7B outperforms the larger proprietary Seed-1.6-embedding model and leads open-source models of comparable size by a substantial margin.
Accurate Multilingual, Multimodal Retrieval
Understands multilingual text, images, and video—including English and Chinese—and retrieves across languages. User instructions can precisely filter content by modality and type.
Storage and Compute Efficiency
Matryoshka-style dimension truncation reduces vector storage and retrieval compute. Built-in lossless int8 quantization cuts storage by more than 50% without reducing retrieval quality.
Designed for Enterprise Document Intelligence
Enterprise knowledge retrieval builds more precise, comprehensive, and intelligent knowledge services that help AI drive business growth.