| Property | Value |
|---|---|
| Parameter Count | 4B |
| Model Type | Mixture of Experts (MOE) |
| Context Length | 128k tokens |
| Base Architecture | Qwen 2.5 |
What is Qwen2.5-MOE-2X1.5B-DeepSeek-Uncensored-Censored-4B-gguf?
This innovative model represents a unique approach to language model architecture, combining two 1.5B parameter Qwen 2.5 DeepSeek models - one censored and one uncensored - into a 4B parameter Mixture of Experts (MOE) system. The model leverages a shared expert architecture, with the uncensored version taking precedence in decision-making processes.
Implementation Details
The model utilizes a specialized Jinja Template encoding in the GGUF format, with compatibility for Llama 3 and Chatml templates as fallback options. It's optimized for deployment in various AI applications, with specific focus on mathematical and logical reasoning tasks inherited from its DeepSeek Qwen 1.5B foundation.
- Unique MOE architecture combining censored and uncensored variants
- Enhanced reasoning capabilities through dual model integration
- Optimized for Q4/IQ4 or higher quantization
- 128k context window support
Core Capabilities
- Advanced mathematical and logical problem solving
- Scientific reasoning and analysis
- Flexible template compatibility
- Extended context processing
- Balanced content generation between censored and uncensored approaches
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its MOE architecture that combines two specialized variants of the Qwen 2.5 model, creating enhanced reasoning capabilities while maintaining flexibility in content generation approaches.
Q: What are the recommended use cases?
The model excels in mathematical and logical reasoning tasks, scientific analysis, and general-purpose text generation. It's particularly effective when detailed prompts are provided and higher quantization levels are used.