There are thousands of LLMs, especially transformer models, that are publicly available. In particular, we use the Mistral AI 7 billion parameter model, which employs Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning (PEFT) 42 adopted from the UnslothAI package. Mistral is a powerful model with 7. Now, fine-tuning requires transforming the instructions into a specialized protocol such as Alpaca. The Alpaca instructions consist of Python dictionaries with keys for instruction, input, and output texts. The instruction key was set to "Below is a description of a material." The XRD patterns were interpolated on a grid of 180 points, with intervals of 0.5 • 2θ, using three floating-point precision, and then converted to a string with a newline character as separators. A fixed pattern length allows for uniform token lengths for LLMs, irrespective of different simulation and experimental settings for PXRD data. Note that with decreasing intervals (here 0.5), the number of tokens increases, and hence, the training and inference time will be higher. The input key used was of three types: 1) with no chemical information,