Software testing for mobile applications is a time-consuming and costly affair due to a high degree of manual testing. Consequently, researchers and corporate enterprises try to find solutions to automate the generation of test cases for mobile applications. Therefore, this work introduces fine-tuned Transformer models, called TestGen-Dart, for the downstream task of generating test cases for mobile applications in Dart. TestGen-Dart_v0.2 enhanced the generation of syntactically correct unit tests by 15.38% and functionally correct unit tests by 16.67%, compared to the underlying base model, Code Llama 13B. This evidenced that supervised fine-tuning (SFT) increases the capability of transformer-based LLMs in a specific downstream task, in this instance, generating test cases for mobile applications. TestGen- Dart_v0.2 also outperformed the state-of-the-art models LLaMA 2 13B and Mistral 7B in that task. Furthermore, the size of 13B parameters of the TestGen-Dart models enables it to run locally on standard consumer hardware, making it a cost-efficient and privacy-preserving testing assistant for software developers by avoiding an external server connection to run the model.