Description:

The Qwen3-Embedding-0.6B-GGUF is a specialized text embedding model, part of the Qwen3 series, tailored for efficient and effective text embedding and reranking tasks.

Key Features & Use Cases:

• Model Type: Primarily a Text Embedding model, also part of a series that includes Text Reranking models.
• Size: A compact model with 0.6 billion parameters, making it suitable for scenarios where efficiency is prioritized. Other sizes (4B, 8B) are available in the series for different performance needs.
• Multilingual Capability: Supports over 100 languages, including various programming languages, excelling in multilingual, cross-lingual, and code retrieval scenarios.
• Context Length: Capable of processing texts up to 32,000 tokens.
• Embedding Dimension: Offers flexible output dimensions, allowing users to define embeddings from 32 to 1024.
• Instruction Awareness: Supports user-defined instructions to optimize performance for specific tasks, languages, or use cases. This can lead to a 1% to 5% improvement in performance for most downstream tasks, especially in retrieval scenarios.
• Quantization: Available in q8_0 and f16 quantization formats.
• Applications: Ideal for various text embedding and ranking tasks, including:
  • Text Retrieval
  • Code Retrieval
  • Text Classification
  • Text Clustering
  • Bitext Mining

Usage Notes:

• When using the model, it’s recommended to append the <|endoftext|> token to your input context.
• For optimal results, tailor your input instructions (instruct) to the specific task or language.

HuggingFace