LUMINA-8b-NSFW-modelfiles-v2

Finetuned LLaMA3 8B 8bit Quantized Model for NSFW Tasks 🚀

Welcome to the repository for our finetuned LLaMA3 8B 8bit quantized model! This model has been carefully optimized for efficiency and high performance in handling NSFW tasks using state-of-the-art 8-bit quantization.

Table of Contents

• Model Details
  • Model Description
  • Development and Funding
  • Model Architecture
  • License
  • Finetuned From
  • Model Sources (Optional)
• Intended Uses
  • Primary Use Cases
  • Out-of-Scope Use
• Bias, Risks, and Limitations
• Disclaimer

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Model Details

Model Description

This repository hosts a finetuned LLaMA3 8B 8bit quantized model specifically designed for NSFW tasks. By leveraging 8-bit quantization, we achieve a balance between model performance and resource efficiency, making it an excellent choice for scalable deployments.

Development and Funding

• Developed by: LUMINA AI LLC 💡
• Funded by: LUMINA AI LLC 💰

Model Architecture

• Model Type: Transformer (LLaMA3)
• Parameters: 8 Billion (8bit quantized)
• Performance: Optimized for fast inference with a reduced memory footprint.

License

• License: MIT License 📝

Finetuned From

• Base Model: meta-llama/Meta-Llama-3-8B-Instruct

Model Sources (Optional)

For additional context or updates, please refer to:

• Repository: meta-llama/Meta-Llama-3-8B-Instruct

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Intended Uses

Primary Use Cases

• General Instruction-based Tasks: Designed to follow a variety of instructions.
• NSFW Text Generation: Capable of generating NSFW texts in a controlled and safe manner.

Out-of-Scope Use

• Prohibited Content: This model should not be used to produce harmful, abusive, or inappropriate content that promotes hate, violence, or any explicit harmful messages.

Installation

Visit the Ollama GitHub page (https://github.com/ollama/ollama?tab=readme-ov-file#customize-a-model) and check "import from gguf" or "import from safetensors".

Bias, Risks, and Limitations

• Content Restrictions: Although this model is finetuned to allow NSFW texts, it has built-in safeguards to restrict harmful explicit content.
• Bias Awareness: As with many language models, it may inherit biases from its training data. Users are advised to review and monitor its outputs carefully.
• Limitations: Performance may vary based on context, and the model is best suited for general instruction tasks. Always validate critical outputs, especially in sensitive applications.

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Disclaimer

While we've made efforts to ensure the model's responsible usage, it remains the user's responsibility to apply it ethically. Neither the developers nor the funders assume liability for any misuse or unintended consequences of the model's outputs.

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Happy experimenting and stay innovative! 🚀✨