qwen3 vl embedding

We have hosted the application qwen3 vl embedding in order to run this application in our online workstations with Wine or directly.

Quick description about qwen3 vl embedding:

Qwen3-VL-Embedding (with its companion Qwen3-VL-Reranker) is a state-of-the-art multimodal embedding and reranking model suite built on the open-sourced Qwen3-VL foundation, developed to handle diverse inputs including text, images, screenshots, and videos. The core embedding model maps such inputs into semantically rich vectors in a unified representation space, enabling similarity search, clustering, and cross-modal retrieval. The reranking model then precisely scores relevance between a given query and candidate documents, enhancing retrieval accuracy in complex multimodal tasks. Together, they support advanced information retrieval workflows such as image-text search, visual question answering (VQA), and video-text matching, while providing out-of-the-box support for more than 30 languages.

Features:

• Unified multimodal embedding for text, images, and video
• High-precision reranking model for relevance scoring
• Support for single and mixed modality inputs
• Flexible vector dimensions with Matryoshka Representation Learning
• Multilingual support for global applications
• Easy integration into existing retrieval pipelines

Programming Language: Python.
Categories: