Model Overview
The query-crafter-japanese-Qwen3-1.7B is a lightweight, 1.7 billion parameter model developed by Yuichi Tateno, optimized for generating high-quality search queries from Japanese text. It is part of a collection of models designed to efficiently create diverse question-query pairs for information retrieval tasks, particularly for training vector search systems and rerankers, without the overhead of larger commercial LLMs.
Key Capabilities
- Diverse Query Generation: Produces seven distinct categories of queries from Japanese documents, including
keywords, synonym_keywords, query, alt_query, title, faq, and summary. - High Performance: Achieves an average score of 0.8701 in relevance evaluation against DeepSeek-R1 using the BAAI/bge-reranker-v2-m3, demonstrating competitive quality.
- Exceptional Speed & Cost-Efficiency: Processes approximately 48,000 tokens/second for input and generates 2,200 tokens/second for output, enabling query generation for 10,000 documents in under 100 seconds. Offers significant cost savings compared to API services for large-scale query generation.
- Apache 2.0 License: Available for unrestricted use in projects.
Ideal Use Cases
- Training Vector Search Systems: Generating synthetic query data to improve the performance of vector search and retrieval-augmented generation (RAG) systems.
- Reranker Training: Creating diverse query-document pairs for fine-tuning reranking models.
- Large-Scale Japanese Information Retrieval: Efficiently generating queries for vast collections of Japanese documents where speed and cost are critical factors.