LLM routing is a solution where queries are directed to different language models based on their complexity, aiming to minimize costs while maintaining response quality. RouteLLM is a framework for LLM routing based on preference data, addressing the challenge of inferring query characteristics and model capabilities. By training four different routers using public data, significant cost reductions were achieved without compromising quality, with up to 85% cost reduction on MT Bench, 45% on MMLU, and 35% on GSM8K compared to using only GPT-4, while still achieving 95% of GPT-4’s performance. The code and datasets, along with an open-source framework for serving and evaluating LLM routers, have been publicly released. Support for the channel is available through Patreon and Ko-Fi, and the creator can be followed on Twitter and LinkedIn.
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Reduce LLM API expenses using LLM Router solution! #costsavings
Preeti
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