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Exploring LLMs, Knowledge Graphs, and Quantization Methods for Model Optimization
This chapter explores the intricacies of fine-tuning language models to enhance efficiency, focusing on the utilization of reliable data to prevent errors such as hallucination. It also discusses the amalgamation of LLMs with knowledge graphs and vector search techniques, along with the application of quantization methods for optimizing models across different hardware platforms.