DeepSeek-R1
| Property | Value |
|---|---|
| Total Parameters | 671B |
| Activated Parameters | 37B |
| Architecture | Mixture of Experts (MoE) |
| Context Length | 128K tokens |
| License | MIT License |
What is DeepSeek-R1?
DeepSeek-R1 represents a groundbreaking advancement in AI language models, particularly in reasoning capabilities. It's a large-scale model trained primarily through reinforcement learning, without the traditional supervised fine-tuning step. The model demonstrates exceptional performance across mathematical reasoning, code generation, and complex problem-solving tasks.
Implementation Details
The model utilizes a unique training pipeline incorporating two RL stages for discovering improved reasoning patterns and aligning with human preferences. It's built on the DeepSeek-V3-Base architecture and includes various distilled versions ranging from 1.5B to 70B parameters.
- Advanced MoE architecture with 671B total parameters but only 37B activated parameters
- 128K token context length for handling extensive conversations
- Specialized training pipeline combining reinforcement learning with selective supervised fine-tuning
- Multiple distilled versions available for different computational requirements
Core Capabilities
- Outstanding performance in mathematical reasoning (97.3% on MATH-500)
- Strong coding abilities (2029 rating on Codeforces)
- Advanced multilingual support with strong performance in both English and Chinese tasks
- Self-verification and reflection capabilities
- Long chain-of-thought reasoning generation
Frequently Asked Questions
Q: What makes this model unique?
DeepSeek-R1 is the first open research to validate that reasoning capabilities can be developed purely through reinforcement learning, without requiring supervised fine-tuning. This breakthrough approach has led to naturally emerging reasoning behaviors and exceptional performance across various benchmarks.
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 solving, code development, and advanced mathematical computations. For optimal results, it's recommended to use a temperature setting of 0.5-0.7 and include specific reasoning directives in prompts.
