QwenLM/Qwen3-Embedding

Health v2 · maintenance only

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Adoption not part of the score

2016 stars · 133 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Qwen3-Embedding is a series of text embedding and reranking models (0.6B, 4B, 8B) built on Qwen3 foundation models, with a Python repository providing usage examples and tooling. The models support 100+ languages, instruction-aware embeddings, and flexible vector dimensions (MRL).

Use cases

• generate text embeddings for semantic search
• rerank search results for retrieval pipelines
• build multilingual retrieval across 100+ languages
• retrieve code snippets by semantic similarity
• cluster and classify documents by embedding
• mine bitext pairs for translation

When to choose

• you need state-of-the-art multilingual embeddings with MTEB-leading quality
• you want instruction-aware embeddings tunable per task
• you need flexible embedding dimensions for storage/latency tradeoffs
• you want to pair embedding and reranking models in a RAG pipeline

When to avoid

• you need a tiny low-latency embedding model for CPU-only deployment
• you require a permissive license for commercial use without verification
• you just need a hosted embedding API without running models yourself

Facets

library · maturity active

machine-learning search-engine rag nlp large-language-models python cross-platform text-embedding reranking multilingual mteb sentence-embeddings huggingface natural-language-processing search retrieval-augmented-generation gpu

Member repositories

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markdown · JSON · MCP: product_card(name="QwenLM/Qwen3-Embedding")