turn detector G G U F

QuantFactory

Introduction

The turn-detector-GGUF model is a quantized version of the livekit/turn-detector model, created using llama.cpp. This model is part of the Hugging Face Transformers library and is designed for conversational applications.

Architecture

Details regarding the model architecture and specific objectives are not provided in the documentation. More information is needed to fully understand the technical specifications and the underlying architecture of this model.

Training

Training Data

Information about the training data used for this model is not provided.

Training Procedure

The documentation lacks details on the preprocessing steps, training hyperparameters, and the overall training regime. Additional specifics would be necessary to replicate or understand the model's training process.

Evaluation

Details regarding testing data, factors, metrics, and results are not included in the documentation. Therefore, insights into the model's performance and evaluation are limited.

Guide: Running Locally

  1. Setup Environment: Ensure that Python and the necessary libraries, such as transformers, are installed.
  2. Download the Model: Use the Hugging Face Hub to download the turn-detector-GGUF model to your local machine.
  3. Load Model: Use the transformers library to load the model.
  4. Inference: Run the model on your data inputs to perform conversational tasks.

For enhanced performance, it is recommended to use cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The license information for the turn-detector-GGUF model is not specified in the documentation. Users should verify the licensing terms before use.

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