Mockingbird` is a Retrieval Augmented Generation (RAG) and structured output focused Large Language Model (LLM), developed by Vectara, designed to provide high-quality summaries and answers for complex tasks such as RAG and structured output. The model is trained on diverse datasets with complexities in input and good output summaries with citations, ensuring it can handle various scenarios and domains. Mockingbird's performance is evaluated using automated metrics and human ratings, showcasing its ability to outperform competitive models in generating high-quality summaries and answers. With a smaller parameter count of <10B compared to larger models like GPT-4 or Gemini 1.5 Pro, Mockingbird demonstrates competitiveness without sacrificing quality, making it an attractive option for customers seeking a task-focused LLM.