Llama 3.2 4 X3 B M O E Hell California Uncensored 10 B G G U F

Introduction

LLAMA-3.2-4X3B-MOE-HELL-CALIFORNIA-UNCENSORED-10B-GGUF is a Llama 3.2 model designed for creative writing and storytelling. It leverages a mixture of experts (MOE) approach to combine four top 3B models into one, resulting in a highly capable language model with 10 billion parameters. This model excels in generating uncensored, vivid prose across various genres.

Architecture

The model utilizes a mixture of experts (MOE) architecture, integrating four distinct 3B models to create a single 10B parameter powerhouse. This architecture allows for dynamic expert selection during generation, enhancing both the diversity and quality of the outputs. The model supports a maximum context length of 128k tokens and is optimized for creative tasks such as fiction writing and role-playing.

Training

The LLAMA-3.2-4X3B-MOE-HELL-CALIFORNIA model is trained on a diverse dataset with a focus on uncensored content, enabling it to produce detailed and engaging narratives. It supports various temperature settings, offering flexibility in output creativity and style. The model is designed to handle a wide range of prompts and maintains low perplexity levels, outperforming many other Llama models.

Guide: Running Locally

1. Set Up Environment: Ensure that you have a suitable environment for running large language models, preferably with a high-end GPU.
2. Download Model: Obtain the model files from the Hugging Face repository.
3. Install Dependencies: Use Python and necessary libraries such as PyTorch or TensorFlow.
4. Run Model: Use a compatible interface, such as LMStudio or text-generation-webui, to load and run the model. Set the number of experts and tweak parameters like temperature and sampling for desired output.
5. Suggestion: For optimal performance, consider using cloud GPUs like those provided by AWS, Google Cloud, or Azure.

License

The model is released under the Apache 2.0 License, permitting open usage, modification, and distribution with proper attribution.