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  # LLM user flow classification
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- This a ONNX quantized model and is fined-tuned version of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
 
 
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- This model identifies common events and patterns within the conversation flow. Such events include, for example, complaint, when a user expresses dissatisfaction.
 
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  This model is used *only* for the user texts. For the LLM texts in the dialog use this [agent model](https://huggingface.co/minuva/MiniLMv2-agentflow-v2-onnx).
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  # Deployment
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- Check [our llm-flow-classification repository](https://github.com/minuva/llm-flow-classification) for a FastAPI and ONNX based server to deploy this model on CPU devices.
 
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  # LLM user flow classification
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+ This model identifies common events and patterns within the conversation flow.
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+ Such events include, for example, complaint, when a user expresses dissatisfaction.
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+ The flow labels can serve as foundational elements for sophisticated LLM analytics.
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+ It is ONNX quantized and is a fined-tune of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
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+ The base model can be found [here](https://huggingface.co/minuva/MiniLMv2-userflow-v2)
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  This model is used *only* for the user texts. For the LLM texts in the dialog use this [agent model](https://huggingface.co/minuva/MiniLMv2-agentflow-v2-onnx).
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  # Deployment
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+ Check our [llm-flow-classification repository](https://github.com/minuva/llm-flow-classification) for a FastAPI and ONNX based server to deploy this model on CPU devices.