turn detector G G U F
QuantFactoryIntroduction
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
- Setup Environment: Ensure that Python and the necessary libraries, such as
transformers
, are installed. - Download the Model: Use the Hugging Face Hub to download the
turn-detector-GGUF
model to your local machine. - Load Model: Use the
transformers
library to load the model. - 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.