Qwen3 embedding and reranking models for retrieval are now available in Amazon SageMaker JumpStart
Amazon SageMaker JumpStart now offers Qwen3-VL-Embedding-2B and Qwen3-Reranker-4B models for information retrieval and cross-modal understanding, enabling customers to build comprehensive search pipelines on AWS infrastructure.
AWS has announced the availability of Qwen3-VL-Embedding-2B and Qwen3-Reranker-4B models in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These models from Qwen are designed for information retrieval and cross-modal understanding, enabling customers to build comprehensive search pipelines on AWS infrastructure. The Qwen3-VL-Embedding-2B model accepts diverse inputs including text, images, screenshots, and videos, generating semantically rich vectors that capture both visual and textual information in a shared space. The Qwen3-Reranker-4B model takes a query and document pair as input and outputs a precise relevance score to refine retrieval results. With SageMaker JumpStart, customers can deploy these models with just a few clicks to address their specific AI use cases.