In our study, we employed a autoregessive attention only network , recognized for its robustness in modeling complex data relationships across fields such as natural language processing and cheminformatics. This architecture's effectiveness lies in its capacity to handle intricate dependencies and interactions, making it ideal for the nuanced task of generating molecules with specific characteristics. The core configuration of the model includes a hidden size of 512, which provides substantial capacity to understand complex data relationships while maintaining manageability on standard computational hardware. Additionally, the model features a multi-head attention mechanism with 16 heads, allowing simultaneous processing of diverse data facets, and is built with a depth of 24 layers to capture deep hierarchical patterns essential for accurate molecule synthesis. Despite its robustness, this model configuration is smaller compared to larger language models used in broader tasks, a design choice driven by the specific requirements of molecule generation. This smaller scale ensures the model remains both efficient and practical for the task at hand, balancing computational demands with performance. Compared to larger models, our transformer demonstrates a strong capability to predict molecular structures in response to set conditions such as molecular weight, atom count, and synthetic accessibility. The decision to use a relatively compact model configuration was informed by extensive hyperparameter tuning and existing research, which suggested diminishing returns for larger models in tasks of similar complexity. This strategic choice ensures that our model can be effectively trained and utilized with the available resources, making it well-suited for iterative development and real-world applications, thus providing a powerful yet computationally feasible tool for exploring complex relationships in molecule generation tasks.