qwen3 embedding

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

Quick description about qwen3 embedding:

Qwen3-Embedding is a model series from the Qwen family designed specifically for text embedding and ranking tasks. It builds upon the Qwen3 base/dense models and offers several sizes (0.6B, 4B, 8B parameters), for both embedding and reranking, with high multilingual capability, long?context understanding, and reasoning. It achieves state-of-the-art performance on benchmarks like MTEB (Multilingual Text Embedding Benchmark) and supports instruction-aware embedding (i.e. embedding task instructions along with queries) and flexible embedding/vector dimension definitions. It is meant for tasks such as text retrieval, classification, clustering, bitext mining, and code retrieval.

Features:

• Multiple model sizes (0.6B, 4B, 8B) for embedding and reranking variants
• Multilingual support over 100 languages and dialects, including many low-resource ones; also supports programming languages in code retrieval tasks
• Supports long input context lengths (up to 32K tokens in many cases) for better handling of longer texts
• Instruction?aware: you can augment input with task instructions to improve performance in specific domains or tasks
• Supports custom embedding dimensions via Matryoshka Representation Learning (MRL) in some variants
• High benchmark performance: e.g. the 8B embedding model ranks No.1 in MTEB multilingual leaderboard as of June 5, 2025; reranker models excel in retrieval contexts

Programming Language: Python.
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