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.
References:
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.
Chollet, F. (2017). Deep Learning with Python. Manning Publications.
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