Omaratef3221/Qwen2-0.5B-Instruct-SQL-query-generator
The Omaratef3221/Qwen2-0.5B-Instruct-SQL-query-generator is a 0.5 billion parameter Qwen2-Instruct model, fine-tuned by Omaratef3221. This specialized model excels at generating SQL queries from natural language text prompts, leveraging a 131072 token context length. Its primary differentiator is its optimization for text-to-SQL conversion, making it suitable for building natural language interfaces for databases.
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Model Overview
The Omaratef3221/Qwen2-0.5B-Instruct-SQL-query-generator is a specialized 0.5 billion parameter model, fine-tuned from Qwen/Qwen2-0.5B-Instruct. Its core function is to convert natural language text into SQL queries. The model was trained on the first 10,000 rows of the motherduckdb/duckdb-text2sql-25k dataset, focusing on text-to-SQL generation.
Key Capabilities
- Natural Language to SQL Conversion: Translates text prompts into executable SQL queries.
- Database Interaction: Facilitates data retrieval from databases using natural language commands.
- Interface Building: Supports the development of natural language interfaces for database systems.
Training Details
The model was fine-tuned with a learning rate of 1e-4, a batch size of 8, and 5 epochs. While specific performance metrics like accuracy, precision, recall, and F1-score were used for evaluation, their values are not explicitly provided in the README. Users should review generated SQL queries for accuracy and security before execution.
Good For
- Developers needing to integrate natural language query capabilities into database applications.
- Prototyping text-to-SQL features where a compact, specialized model is beneficial.
- Educational purposes to understand fine-tuning for specific NLP tasks.