CrysText represents a significant advancement in materials informatics, demonstrating the potential of fine-tuned LLMs to generate crystal structures directly from textual descriptions. By leveraging the efficiency of QLoRA and the generative power of Llama-3.1-8B, CrysText provides a scalable and cost-effective solution for generating CIF-formatted structures that adhere to specified compositions and symmetries. Evaluations on the MP-20 dataset highlight the CrysText's strong performance in generating stable and realistic crystal structures, showcasing its transformative role in text-prompted inverse design. Despite these achievements, limitations remain. The current model focuses on specific tasks such as conditioning on composition and space group, with scope for improvement through broader task integration, enhanced LLM architectures, and extensive hyperparameter optimization.