Addressing this gap, Large Language Models (LLMs) offer a transformative alternative by enabling direct and intuitive user interaction through text-based inputs. Pre-trained on vast and diverse corpora, LLMs inherently excel at processing and generating natural language, making them particularly suited for tasks requiring seamless and dynamic interaction via text prompts . However, adapting LLMs for domain-specific applications such as crystal structure prediction requires overcoming the computational challenges posed by their size and complexity. Parameter-Efficient Fine-Tuning (PEFT) techniques , such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA) , address this issue by significantly reducing the number of trainable parameters. These methods allow for efficient adaptation of LLMs to specialized tasks, making fine-tuning more accessible and scalable without requiring extensive computational resources.