Crystal-LLM demonstrates that fine-tuned LLMs can generate the three-dimensional structure of stable inorganic materials as text. LLMatDesign 21 is a language-based framework for interpretable materials design, powered by LLMs. LLMatDesign leverages LLM agents to interpret human instructions, apply modifications to materials, and evaluate the results. AtomGPT 22 employs intermediate properties, such as band gap or superconducting temperature, to assist in structure generation. AtomGPT operates in two modes: mapping structure descriptions to property regression, and mapping descriptions of a compound and its associated property to a crystal structure. LLM-Prop 23 generated a dataset of inorganic crystal text descriptions sourced from the Materials Project and describes them with Robocrystallographer. LLM-Prop predicts physical and electronic properties from these descriptions. Chemeleon is a Graphical Neural Network that uses GPT-generated textual descriptions of crystals sourced from the Ma-terials Project database to strengthen the relationship between composition and structure in the task of sampling novel chemical compositions and crystal structures. CrysText 25 is a fine-tuned LLAMA model that uses text containing a space group and formula to generate CIF-formatted crystal structures. The CrysText work has some commonalities with our approach, but a notable difference is that our work uses a significantly smaller model (350M parameters versus 8B) and our model also was initially trained for code generation.