DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | Instruction-tuned Language Model |
| Architecture | LLaMA 3.1 |
| License | LLaMA 3.1 |
| Supported Languages | 11 languages including English, German, French, Italian, Portuguese, Hindi, Spanish, Thai, Chinese, Korean, Japanese |
What is DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored?
DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored is a specialized variant of Meta's LLaMA 3.1 architecture, optimized for creative and roleplay applications. This model features an 8B parameter architecture with a 128k context window, supporting multilingual capabilities across 11 languages. It's specifically designed to provide unrestricted creative outputs while maintaining high performance across various tasks.
Implementation Details
The model utilizes the LLaMA 3.1 architecture with BF16 tensor type implementation. It requires transformers version >= 4.43.1 and has been specifically readjusted for mobile phone compatibility. The model features specialized module combinations for enhanced role-playing capabilities and creative writing tasks.
- 128k context window support
- BF16 precision for optimal performance
- Optimized for both mobile and desktop platforms
- Comprehensive multilingual support
Core Capabilities
- Advanced roleplay and creative writing
- Multilingual text generation across 11 languages
- Quick response generation
- Scholarly response generation
- Code generation and completion
- Specialized role-playing scenarios
- Uncensored creative content generation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized optimization for roleplay and creative writing tasks, combined with comprehensive multilingual support and uncensored generation capabilities. It represents a balance between performance and accessibility with its 8B parameter size.
Q: What are the recommended use cases?
The model is primarily designed for creative writing, roleplay scenarios, multilingual text generation, and academic-style content creation. It's particularly well-suited for applications requiring unrestricted creative expression while maintaining coherent and contextually appropriate outputs.
