DeepSeek-V3-0324-GGUF
| Property | Value |
|---|---|
| License | MIT |
| Author | Unsloth |
| Paper | arXiv:2412.19437 |
| Recommended Quantization | 2.42-bit (IQ2_XXS) or 2.71-bit (Q2_K_XL) |
What is DeepSeek-V3-0324-GGUF?
DeepSeek-V3-0324-GGUF is an advanced language model that represents a significant improvement over its predecessor, featuring Unsloth's Dynamic Quants technology for selective quantization. The model is available in various compression levels ranging from 1.78 to 4.5 bits, allowing users to balance performance and resource requirements.
Implementation Details
The model implements a sophisticated quantization strategy with different bits for different components, particularly in the MoE (Mixture of Experts) down_proj layers. It requires at least 180GB+ combined VRAM + RAM for optimal performance, with recommended temperature settings of 0.3 for most use cases.
- Multiple quantization options from 173GB to 406GB disk size
- Specialized handling of MoE architecture components
- Support for function calling, JSON output, and FIM completion
- Implementation with llama.cpp, LMStudio, and Open WebUI compatibility
Core Capabilities
- Enhanced reasoning with significant improvements in MMLU-Pro (+5.3), GPQA (+9.3), and AIME (+19.8)
- Advanced front-end web development with improved code executability
- Superior Chinese language capabilities including writing and search functionalities
- Optimized translation quality and letter writing
- Improved function calling accuracy
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its use of Dynamic Quants technology, which provides better accuracy than standard quantization methods while maintaining efficient compression. It also shows remarkable improvements in reasoning benchmarks and specialized capabilities in both English and Chinese content generation.
Q: What are the recommended use cases?
The model excels in front-end web development, technical reasoning tasks, Chinese content creation, and multi-language translation. It's particularly well-suited for applications requiring strong reasoning capabilities, as evidenced by its improved benchmark performances in MMLU-Pro, GPQA, and AIME.
