What is the primary purpose oi inferencing in the lifecycle of a Large Language Model (LLM)?

Inferencing in the lifecycle of a Large Language Model (LLM) refers to using the model in practical applications. Here’s an in-depth explanation: Inferencing: This is the phase where the trained model is deployed to make predictions or generate outputs based on new input data. It is essentially the model’s application stage. Production Use: In production, inferencing involves using the model in live applications, such as chatbots or recommendation systems, where it interacts with real users. Research and Testing: During research and testing, inferencing is used to evaluate the model’s performance, validate its accuracy, and identify areas for improvement.
Reference: LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. Chollet, F. (2017). Deep Learning with Python. Manning Publications.