OpenSearch AI/Ops Colqwen3 4B Ops Colqwen3 4B is a ColPali style multimodal embedding model based on the Qwen3 VL 4B Instruct architecture, developed and open sourced by the Alibaba Cloud OpenSearch AI team. It maps text queries and visual documents such as images and PDF pages into a unified, aligned multi vector embedding space , enabling highly effective retrieval of visual documents. The model is trained using a multi stage strategy that combines large scale text based retrieval datasets with diverse visual document data. This hybrid training approach significantly enhances its capability to handle complex document understanding and retrieval tasks. On the Vidore v1–v3 benchmarks, Ops Colqwen3 4B achieves state of the art results among models of comparable size. Key Features Model size : 4 billion parameters Multimodal alignment : Enables fine grained semantic alignment between text and images or PDF pages Multi vector embeddings : Following the ColPali design, each input generates multiple context aware embedding vectors; similarity is computed using MaxSim , enabling high precision matching Scalable embedding dimensions : Supports embedding dimensions up to 2,560 during infer…
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