When selecting parameters to optimize a prompt -tuned model experiment in IBM watsonx, which parameter is the most critical for controlling the model’s ability to generate coherent and contextually accurate responses?
After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)
inference?
You are tasked with fine -tuning prompts for a customer support chatbot built using IBM Watsonx. You decide to leverage Prompt Lab to improve the model's responses. Which of the following best describes the key benefits of using Prompt Lab for this task?
responses. You are tasked with deploying a foundation model for text generation on the IBM Watsonx platform. The foundation model has been pre -trained on a large corpus but has not been fine -tuned for your specific use case. What is the most critical factor to consider when deploying this model to ensure it performs optimally on the Watsonx platform?
B. "Generate a product description that highlights the unique aspects of the product and uses emotional language to engage the reader." C. "Write a summary that provides information on each product, making the content engaging, humorous, and memorable." D. "Provide a product description for the following items, ensuring it is factual, concise, and includes specific details such as size, color, and material." Which of the following statements best describes the primary advantage of applying quantization to a large language model (LLM) during inference?
