Qwen2.5 14 B Vimarckoso v3 i1 G G U F

Introduction

The QWEN2.5-14B-VIMARCKOSO-V3-I1-GGUF is a quantized model based on the Qwen2.5-14B architecture, optimized for various inference scenarios. It is available on the Hugging Face platform and supports English language processing using the Transformers library.

Architecture

This model utilizes the GGUF format, providing a range of quantization types, including static and imatrix weighted quants. The architecture is designed to be flexible, supporting various quality and size combinations, enabling users to choose the best configuration for their specific needs.

Training

The model is based on the Qwen2.5-14B architecture, initially provided by sometimesanotion. It has been further quantized by mradermacher, who has optimized it for different use cases, ensuring efficient performance across a range of hardware configurations.

Guide: Running Locally

To run the model locally, follow these steps:

1. Clone the repository: Download the model files from Hugging Face.
2. Set up the environment: Ensure you have the necessary dependencies installed, particularly the Transformers library.
3. Select a quant: Choose the appropriate GGUF file based on your performance and quality needs.
4. Load the model: Use the Transformers library to load and run the model for your application.

For optimal performance, especially with larger model sizes, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The model is distributed under the Apache 2.0 License, allowing for broad usage, including commercial applications, with proper attribution.