We have demonstrated the application of a fine-tuned LLaMA model for the extraction of structured reaction information from unstructured reaction texts from the USPTO. The fine-tuned model can consistently (99.63%) produce JSON records complying with the highly structured ORD data schema. The fine-tuned model exhibits average accuracies of 91.25% for message level, and 92.25% for field-level extractions. The fine-tuned model can also infer reaction roles that are not explicitly stated in texts, modestly beating the popularity baseline for role classification. While the model may not be accurate enough to be directly used in dataset preparation, it may greatly accelerate information extraction compared to manual extraction, and simplify the job of human curators, especially for detailed, nested data schemas.