It sounds like you're interested in understanding the LLM & RAG Model. However, the provided description, " Model ", is quite vague and doesn't give much information to work with. LLM (Log-Linear Models) and RAG (Recursive Auto-associative Graphs) Model are both technical concepts in the field of machine learning and data analysis. LLM is a type of model used in statistical analysis that relates independent variables to a target (dependent) variable. It is commonly used in regression analysis to predict outcomes based on input variables. RAG Model typically refers to Recursive Autoassociative Graphs, which are neural network models used to learn and represent hierarchical relationships in data. These models are often employed in tasks that involve processing structured data like graphs or sequences. Given these definitions, if you have more specific questions or scenarios in mind related to LLM & RAG models, feel free to ask! I'd be happy to delve deeper into these topics or provide examples to make the concepts more tangible.

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LLM & RAG Model ??

Saurabh Prajapati

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Model

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