DeepSeek-R1-GGUF
| Property | Value |
|---|---|
| Total Parameters | 671B |
| Activated Parameters | 37B |
| Context Length | 128K |
| License | MIT |
| Paper | arXiv:2501.12948 |
What is DeepSeek-R1-GGUF?
DeepSeek-R1-GGUF is a quantized version of the DeepSeek-R1 model, specifically optimized for efficient deployment while maintaining high performance in reasoning tasks. The model represents a significant advancement in AI reasoning capabilities, trained through a combination of reinforcement learning and supervised fine-tuning approaches.
Implementation Details
The model comes in various quantization formats, from 1.58-bit to 2.51-bit versions, offering different trade-offs between model size and accuracy. The implementation supports GPU acceleration and can be run using llama.cpp, with specific optimizations for different hardware configurations.
- Supports multiple quantization levels (UD-IQ1_S through UD-Q2_K_XL)
- Offers context length of 8192 tokens
- Includes CUDA support for GPU acceleration
- Compatible with llama.cpp for efficient inference
Core Capabilities
- Exceptional performance in mathematical reasoning (97.3% on MATH-500)
- Strong code generation abilities (96.3 percentile on Codeforces)
- Advanced problem-solving with step-by-step reasoning
- Multilingual support with strong performance in both English and Chinese tasks
Frequently Asked Questions
Q: What makes this model unique?
DeepSeek-R1 is distinctive for its pure reinforcement learning approach to developing reasoning capabilities, without requiring initial supervised fine-tuning. This results in naturally emerged reasoning behaviors and exceptional performance on mathematical and logical tasks.
Q: What are the recommended use cases?
The model excels in mathematical problem-solving, code generation, and complex reasoning tasks. It's particularly well-suited for applications requiring step-by-step problem decomposition and verification. For optimal results, use a temperature of 0.6 and include specific reasoning directives in prompts.
