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Large language models (LLMs) are revolutionizing software development, enhancing user interactions with tools like LangChain and Semantic Kernel. They can assist in various stages of content creation and streamline complex processes. However, concerns about dependence on LLM providers, content censorship, and customization options have led to a search for open-source alternatives. The article explores a fine-tuning method for training your own LLM, alpaca-lora, offering insights into the process, challenges, and potential solutions, particularly for achieving successful fine-tuning on hardware like V100 GPUs. The goal is to create LLMs that produce coherent and contextually relevant responses while avoiding prompt repetition.

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