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.