CrysText is designed to generate crystal structures from text-based inputs, leveraging an autoregressive modeling approach with advanced fine-tuning techniques. At its core is Llama-3.1-8B, an open-source, pre-trained language model known for handling complex generative tasks . The model was fine-tuned using the unsloth library , which supports the 4-bit quantized version of Meta-Llama-3.1-8B for optimized processing. To accommodate comprehensive inputoutput sequences, the model was configured with a maximum sequence length of 2048 tokens. Fine-tuning was performed using QLoRA, which enhances computational efficiency by introducing low-rank matrices into the transformer's architecture. QLoRA utilizes a rank of 16, allowing for targeted updates to the model while preserving the integrity of its pre-trained parameters. This approach reduces the number of trainable parameters to approximately 41 million, enabling efficient, resource-conscious training without sacrificing performance.