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.
This article provides an in-depth analysis of deep learning model quantization concepts, mainstream approaches, and specific implementations in llama.cpp and vLLM inference frameworks, helping readers understand how to achieve efficient model deployment through quantization techniques.
This article provides a comprehensive guide to vLLM, covering its core PagedAttention technology, architecture design, and practical implementation, helping readers understand this high-performance large language model inference and serving engine.