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  ### Model Description
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  This model is designed for prompt routing to determine whether a prompt should be handled by GPT-4o or GPT-3.5. The goal is to reduce costs, as GPT-4o is significantly more expensive (10x the cost of GPT-3.5).
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  Labels were manually assigned based on specific use cases. You can expand the classification to include other models such as LLaMA or GPT-4, depending on your requirements.
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  - **gpt-4o:** Handles complex questions that require deep thinking and analysis, such as math problems, multiple-choice homework, and tests.
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- - **gpt-3.5:** Handles simpler questions primarily for information retrieval, where answers are readily available in the documents from a Retrieval-Augmented Generation (RAG) system, as well as straightforward code and explanations.
 
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+ ---
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+ license: mit
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+ task_categories:
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+ - text-classification
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+ language:
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+ - vi
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+ - en
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+ tags:
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+ - textclassification
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+ - modelrouting
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+ - routing
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+ - gptrouting
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+ - fewshot
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+ size_categories:
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+ - n<1K
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+ ---
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  ### Model Description
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  This model is designed for prompt routing to determine whether a prompt should be handled by GPT-4o or GPT-3.5. The goal is to reduce costs, as GPT-4o is significantly more expensive (10x the cost of GPT-3.5).
 
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  Labels were manually assigned based on specific use cases. You can expand the classification to include other models such as LLaMA or GPT-4, depending on your requirements.
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  - **gpt-4o:** Handles complex questions that require deep thinking and analysis, such as math problems, multiple-choice homework, and tests.
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+ - **gpt-3.5:** Handles simpler questions primarily for information retrieval, where answers are readily available in the documents from a Retrieval-Augmented Generation (RAG) system, as well as straightforward code and explanations.