This article provides an in-depth analysis of LLM tool calling's core principles, technical implementation, code examples, and best practices, detailing how this mechanism enables large language models to break knowledge boundaries and interact with the external world.
This article provides an in-depth analysis of two key categories of hyperparameters for large language models (LLMs): generation parameters and deployment parameters, detailing their functions, value ranges, impacts, and best practices across different scenarios to help developers precisely tune models for optimal performance, cost, and output quality.