Lumimaid Magnum v4 12 B G G U F

Introduction

The Lumimaid-Magnum-v4-12B-GGUF is a quantized version of the Lumimaid-Magnum-v4-12B model, created through the llama.cpp framework. It combines elements from various models, including Mistral, to enhance performance and capabilities.

Architecture

This model merges components from several base models:

• mistralai/Mistral-Nemo-Instruct-2407
• NeverSleep/Lumimaid-v0.2-12B
• Undi95/LocalC-12B-e2.0
• anthracite-org/magnum-v4-12b

The model employs the transformers library and utilizes the mergekit tool for merging, with additional finetuning performed on the Claude input using the DELLA merge method.

Training

The model was fine-tuned using the DELLA method, specifically on the Claude input, and trained with a context size of 16,000. This approach aims to improve the model's ability to generate coherent and relevant responses based on the given input.

Guide: Running Locally

To run the Lumimaid-Magnum-v4-12B-GGUF model locally, follow these steps:

1. Install Dependencies:

   • Ensure you have Python and the necessary libraries installed, including transformers.

2. Download the Model:

   • Access the model files from the Hugging Face repository and download them to your local machine.

3. Load the Model:

   • Use the transformers library to load the model into your application.

4. Run Inference:

   • Input your data and use the model to generate outputs.

5. Utilize Cloud GPUs:

   • For enhanced performance, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure, especially if dealing with large datasets or requiring faster processing speeds.

License

Please refer to the model's repository for specific licensing information. Licensing details are critical to ensure compliance with usage terms and conditions.