We developed an LLM-based tool that transforms various types of text descriptions of crystals and their transformations into valid crystal structure CIFs. Our detailed approach is illustrated in Figure . The dataset used in this study comprises inorganic crystal structures sourced from The Materials Project Database (MP) and is further enriched through structure transformations such as strain, supercell, vacancy, and substitution. Inspired by the Toolformer 27 approach on teaching LLMs to utilize tools, our method involves instructing LLMs to translate text descriptions into the intermediate layer of Pymatgen code 3 , the execution of which outputs crystal structure in the CIF format. For each structure, we generated four types of text descriptions with different levels of detail: complete symmetrized structure information, a ChatGPT-generated natural text summary, a rule-based crystallography representation from Robocrystallographer , and a single-sentence description containing the formula and space group information. We then fine-tuned two models to enhance performance on this task. For our base model, we selected CodeGen , a family of open-source models designed for program synthesis, and performed parameter-efficient fine-tuning using Low Rank Adaptation (LoRa) .