Neural Daredevil 12b 32k G G U F

Introduction

The NeuralDaredevil-12B-32K-GGUF is a quantized model variant developed by mradermacher, based on the original NeuralDaredevil-12B-32K model. It is designed for efficient processing while maintaining quality in its outputs. Quantization reduces model size and improves speed, making it suitable for various applications.

Architecture

This model is built on the transformers library and uses GGUF as its format. It supports the English language and is related to previous versions like mlabonne's NeuralDaredevil-7B. Multiple quantization types are provided, each optimized for different use-cases regarding size, speed, and quality.

Training

The NeuralDaredevil-12B-32K-GGUF model was quantized by mradermacher, with various quantization types available, such as Q2_K, Q3_K_S, and IQ4_XS, among others. These quant types are sorted by size and quality, with some optimized for speed on specific hardware (e.g., ARM processors) and others recommended for general use due to their balance of speed and quality.

Guide: Running Locally

To run the NeuralDaredevil-12B-32K-GGUF model locally, follow these steps:

1. Install Required Libraries: Ensure you have the transformers library installed. Use a package manager like pip to install it if necessary.

   pip install transformers
   

2. Download the Model: Obtain the desired quantized GGUF file from Hugging Face, selecting the version that best fits your needs based on size and quality.

3. Load the Model: Use the transformers library to load the model in your Python environment.

4. Run Inference: Execute inference using your input data to leverage the model's capabilities.

For better performance, consider using a cloud GPU service such as AWS, Google Cloud, or Azure, which can significantly speed up processing tasks.

License

For licensing details, please refer to the Hugging Face model page, as specific terms and conditions may apply.