We fine-tuned LLMs for specific tasks, recognizing that training a unified model capable of handling multiple tasks would require an extensive dataset and the design of diverse instructions, inputs, and outputs. Additionally, leveraging a broader range of LLM architectures and conducting comprehensive hyperparameter tuning could have further enhanced performance. However, our primary focus was on demonstrating the methodology and showcasing the model's capability to learn crystal symmetry and generate crystal structures conditioned on composition and space group.