Source URL: https://simonwillison.net/2025/Jun/8/qwen3-embedding/#atom-everything
Source: Simon Willison’s Weblog
Title: Qwen3 Embedding
Feedly Summary: Qwen3 Embedding
New family of embedding models from Qwen, in three sizes: 0.6B, 4B, 8B – and two categories: Text Embedding and Text Reranking.
The full collection can be browsed on Hugging Face. The smallest available model is the 0.6B Q8 one, which is available as a 639MB GGUF. I tried it out using my llm-sentence-transformers plugin like this:
llm install llm-sentence-transformers
llm sentence-transformers register Qwen/Qwen3-Embedding-0.6B
llm embed -m sentence-transformers/Qwen/Qwen3-Embedding-0.6B -c hi | jq length
This output 1024, confirming that Qwen3 0.6B produces 1024 length embedding vectors.
These new models are the highest scoring open-weight models on the well regarded MTEB leaderboard – they’re licensed Apache 2.0.
Tags: ai, embeddings, qwen, llm
AI Summary and Description: Yes
Summary: The text discusses the new Qwen3 Embedding models, which consist of three sizes and have been recognized for their high performance on the MTEB leaderboard. These models are significant for professionals in AI and LLM security due to their open-weight licensing and potential applications in NLP tasks.
Detailed Description:
The text presents a new family of embedding models called Qwen3, revealing significant insights that could be beneficial for professionals in AI, particularly in natural language processing and security. Here’s an expanded overview of the major points:
– **Model Variants**: Qwen3 includes various sizes—0.6 billion (B), 4B, and 8B parameters, catering to different computational needs and capabilities.
– **Categories**: The models are divided into two main categories: Text Embedding and Text Reranking, allowing flexibility based on the specific tasks required in AI systems.
– **Performance Metrics**: The 0.6B model achieved an output length of 1024 for embedding vectors, confirming its efficacy for various data embedding purposes.
– **Availability**: The models can be accessed and browsed on Hugging Face, facilitating easy integration into existing workflows.
– **High Ranking on MTEB Leaderboard**: These models have been identified as the highest scoring open-weight models on the MTEB (Multilingual Text Embedding Benchmark) leaderboard, which reflects their superior performance in NLP tasks.
– **Licensing**: The models are licensed under Apache 2.0, promoting open-source usage and further innovation in AI application development.
The relevance of this text for security and compliance professionals lies in the implications of open-weight models, which often require robust security measures to handle potential vulnerabilities in deployment. Understanding the performance and licensing of such models is crucial for ensuring safe, compliant integration into AI solutions.