Shaleen123/gemma4-12b-sql
Shaleen123/gemma4-12b-sql is a 12 billion parameter Gemma 4 model fine-tuned for text-to-SQL tasks, enabling the conversion of natural language questions into executable SQL queries given a database schema. This model specializes in schema-grounded query generation, making it highly effective for applications requiring relational data querying without manual SQL writing. It is designed to assist with natural-language interfaces over databases and SQL drafting, supporting a context length of 32768 tokens.
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Model Overview
Shaleen123/gemma4-12b-sql is a 12 billion parameter model based on Gemma 4, specifically fine-tuned for text-to-SQL generation. Its primary function is to translate natural language questions into correct, executable SQL queries, provided with a database schema. This model is designed to streamline interactions with relational databases by automating SQL query creation.
Key Capabilities
- Schema-grounded SQL Generation: Accurately generates SQL queries based on a given database schema and natural language input.
- Agentic SQL Model: Acts as an intelligent agent for SQL query formulation, returning only the SQL query without additional explanations.
- High Context Length: Supports a context length of 32768 tokens, allowing for complex schemas and detailed questions.
Good for
- Natural-language interfaces: Ideal for building BI tools, chat-with-your-data applications, and other systems that require querying relational data using natural language.
- SQL assistance: Useful for SQL autocomplete and drafting tools, helping analysts and engineers generate initial query drafts.
- Research and Benchmarking: Suitable for academic and industrial research in text-to-SQL, as well as for benchmarking purposes.
Limitations and Risks
Users should be aware of potential limitations such as hallucinated schema elements, ambiguity with vague questions, dialect drift, and increased error rates with highly complex queries. Generated SQL is not inherently safe and requires validation to prevent destructive or expensive operations, and to mitigate prompt injection vulnerabilities. It also inherits biases and limitations from its base model and training data.