The objective of this project is to leverage Gemma for text-to-SQL natural language processing. To accomplish this, we fine-tuned two versions of the Gemma model using distinct datasets and conducted evaluations to assess the performance of the models. Our findings indicate that while Gemma can be fine-tuned to generate SQL quickly, the practicality of the generated SQL falls short of expectations.