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Qwen3-Embedding-8B

Description​

The Qwen3 Embedding model by Alibaba is designed for creating embeddings, which are essential for a retrieval-augmented generation (RAG) system. This model does not support chat interaction. It is used to convert documents and texts into individual vectors, which are stored in a vector database by the application, enabling semantic search. The strength of this model lies in its support of over 100 languages with high performance.

The following limitations apply:

  • Maximum context length: 32,768 tokens
  • Embedding dimension: 4,096
  • The dimensions parameter for dynamically projecting to a lower dimension is not supported

It is recommended to format embeddings for search queries using the following template:

Instruct: {task_description}
Query: {query}

Here, {query} should express the individual search query in one sentence, and {task_description} should describe the task, for example:

Terms of use and licensing​

The general terms of use apply. The model is provided by Alibaba under the Apache 2.0 License, and reuse of the generated content is not subject to any additional restrictions.