Llama 3.2 8 X3 B M O E Dark Champion Instruct uncensored abliterated 18.4 B G G U F LLM Model

Introduction

The LLAMA-3.2-8X3B-MOE-DARK-CHAMPION-INSTRUCT-UNCENSORED-ABLITERATED-18.4B-GGUF model is an advanced text generation model designed for creative writing, fiction, and role-playing. It incorporates a mixture of experts to provide exceptional prose and storytelling capabilities.

Architecture

This model is part of the Llama 3.2 architecture, featuring a mixture of experts approach by combining eight top L3.2 3B models into a single 18.4 billion parameter model. This configuration allows for superior instruction following and creative output in various genres and applications. The model is designed to be flexible with different settings and templates, such as the Llama3 or Command-R template, and supports a range of operations from storytelling to role-playing.

Training

The LLAMA-3.2-8X3B-MOE model employs a mixture of experts strategy, using eight different models as "experts" to enhance output quality. Experts can be dynamically selected, allowing for varied and high-quality text generation. The training process emphasizes low perplexity levels and strong instruction adherence, making the model suitable for both creative and general-purpose text generation.

Guide: Running Locally

Setup Environment: Ensure you have the necessary software installed, such as Python, along with libraries like PyTorch or TensorFlow.
Download Model: Obtain the model files from the Hugging Face model repository.
Load Model: Use a compatible framework like LMStudio, Text-Generation-Webui, or KolboldCPP to load and configure the model.
Configure Experts: Adjust the number of experts based on your hardware capabilities and desired output quality. This can be done at the loading screen of the chosen application.
Run Inference: Begin generating text with the model, adjusting parameters such as temperature and repetition penalty to fine-tune output.
Use Cloud GPUs: Consider using cloud services with GPU support for faster processing and higher efficiency, especially for larger models.