DeepSeek-V2-Chat
| Property | Value |
|---|---|
| Total Parameters | 236B |
| Active Parameters | 21B per token |
| Context Length | 128K tokens |
| License | DeepSeek Model License |
| Paper | arXiv:2405.04434 |
What is DeepSeek-V2-Chat?
DeepSeek-V2-Chat is an advanced Mixture-of-Experts (MoE) language model that represents a significant advancement in efficient AI model design. Built on a 236B parameter architecture, it uniquely activates only 21B parameters per token, resulting in substantial efficiency gains while maintaining high performance across various tasks.
Implementation Details
The model implements innovative architectural features including Multi-head Latent Attention (MLA) and DeepSeekMoE architecture. It was trained on 8.1 trillion tokens and incorporates both Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to enhance its capabilities.
- Employs MLA for efficient key-value cache compression
- Utilizes DeepSeekMoE for optimized training costs
- Supports 128K context window
- Requires 80GB*8 GPUs for BF16 inference
Core Capabilities
- Strong performance in multilingual tasks (English and Chinese)
- Advanced coding capabilities with high scores on HumanEval and MBPP
- Exceptional mathematical reasoning (92.2% on GSM8K after RL)
- Competitive performance in open-ended generation tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its efficient MoE architecture that reduces training costs by 42.5% and KV cache by 93.3% while increasing generation throughput by 5.76 times compared to traditional models.
Q: What are the recommended use cases?
DeepSeek-V2-Chat excels in diverse applications including coding tasks, mathematical problem-solving, multilingual translation, and general conversation. It's particularly strong in both English and Chinese language tasks, making it suitable for cross-lingual applications.
