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Some time ago, I built a predictive LLM router that routes chat requests between small and large LLM models based on prompt classification. It dynamically selects the most suitable model depending on the complexity of the user input, ensuring optimal performance while maintaining conversation context. I also fine-tuned a RoBERTa model to use with the package, but you can plug and play any classifier of your choice.
Some time ago, I built a predictive LLM router that routes chat requests between small and large LLM models based on prompt classification. It dynamically selects the most suitable model depending on the complexity of the user input, ensuring optimal performance while maintaining conversation context. I also fine-tuned a RoBERTa model to use with the package, but you can plug and play any classifier of your choice.
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Project's homepage:
Project's homepage:
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https://devquasar.com/llm-predictive-router/
https://devquasar.com/llm-predictive-router/
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Pypi:
Pypi:
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https://pypi.org/project/llm-predictive-router/
https://pypi.org/project/llm-predictive-router/
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Model:
Model:
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https://huggingface.co/DevQuasar/roberta-prompt_classifier-v0.1
model
DevQuasar/roberta-prompt_classifier-v0.1
https://huggingface.co/DevQuasar/roberta-prompt_classifier-v0.1
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Training data:
Training data:
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https://huggingface.co/datasets/DevQuasar/llm_router_dataset-synth
dataset
DevQuasar/llm_router_dataset-synth
https://huggingface.co/datasets/DevQuasar/llm_router_dataset-synth
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Git:
Git:
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https://github.com/csabakecskemeti/llm_predictive_router_package
https://github.com/csabakecskemeti/llm_predictive_router_package
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Feel free to check it out, and/or contribute.
Feel free to check it out, and/or contribute.
Some time ago, I built a predictive LLM router that routes chat requests between small and large LLM models based on prompt classification. It dynamically selects the most suitable model depending on the complexity of the user input, ensuring optimal performance while maintaining conversation context. I also fine-tuned a RoBERTa model to use with the package, but you can plug and play any classifier of your choice.

Project's homepage:
https://devquasar.com/llm-predictive-router/
Pypi:
https://pypi.org/project/llm-predictive-router/
Model:
https://huggingface.co/DevQuasar/roberta-prompt_classifier-v0.1
Training data:
https://huggingface.co/datasets/DevQuasar/llm_router_dataset-synth
Git:
https://github.com/csabakecskemeti/llm_predictive_router_package

Feel free to check it out, and/or contribute.
👍
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