Qwen2.5 14 B Vimarckoso v3 G G U F

Introduction

Qwen2.5-14B-Vimarckoso-v3-GGUF is a model hosted on Hugging Face, quantized by the user mradermacher. This model is built upon the original Qwen2.5-14B-Vimarckoso-v3 and supports various quantization methods to optimize performance and size.

Architecture

This model leverages the transformers library and is designed for English language tasks. It includes a variety of quantization options, which are sorted by size and potentially quality, to suit different performance needs.

Training

The model and its quantization were facilitated by resources provided by nethype GmbH, enabling the optimization of the model's performance on available hardware. Detailed training procedures are not outlined in the provided documentation.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and necessary libraries such as transformers installed.
2. Download Model: Obtain the model files from the provided links on the Hugging Face repository.
3. Select Quantization: Choose an appropriate quantization based on your hardware capabilities and requirements.
4. Run Inference: Utilize the model within a Python script or interactive environment to perform tasks.

For enhanced performance, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure to handle larger models or more intensive computations.

License

The model is licensed under the Apache-2.0 license, allowing for broad use and modification with attribution.