DeepSeek V2.5

DeepSeek-V2.5 represents a significant advancement in large language model architecture, combining efficient design with powerful capabilities. This 236B parameter Mixture-of-Experts (MoE) model achieves state-of-the-art performance while maintaining computational efficiency through innovative architectural choices, as detailed in the technical report.

Architecture and Design

The model's architecture incorporates two key innovations: Multi-head Latent Attention (MLA) and DeepSeekMoE. While the total parameter count is 236B, only 21B parameters are activated per token, significantly reducing computational requirements. The MLA mechanism compresses the Key-Value (KV) cache into a latent vector, enabling more efficient inference.

Compared to its predecessor, DeepSeek 67B, the V2.5 model achieves remarkable efficiency improvements:

• 42.5% reduction in training costs
• 93.3% decrease in KV cache size
• 5.76x increase in maximum generation throughput

Training and Capabilities

DeepSeek-V2.5 underwent extensive training on a high-quality, multi-source corpus containing 8.1 trillion tokens, with increased Chinese content compared to previous versions. The training process included:

1. Initial pretraining on the diverse corpus
2. Supervised Fine-Tuning (SFT)
3. Reinforcement Learning (RL) optimization

The model supports a context length of 128K tokens and demonstrates strong performance across varying context windows, as shown in the Needle in a Haystack benchmark results:

Performance and Benchmarks

DeepSeek-V2.5 achieves impressive results across various benchmarks, particularly in coding and mathematical tasks. The model outperforms many larger models while using fewer activated parameters:

Comparative performance against other leading models shows:

• Surpasses Qwen1.5 72B on most English, code, and math benchmarks
• Matches or exceeds Mixtral 8x22B on English tasks while showing significantly better Chinese capabilities
• Comparable performance to LLaMA 3 70B on code and math tasks, despite using fewer parameters

The model family also includes DeepSeek-V2-Lite, a smaller 15.7B parameter variant with 2.4B activated parameters, designed for research purposes. The model's code is available under the MIT license, while the model itself has a separate license allowing for commercial applications.

References

• DeepSeek-V2 Technical Report - Detailed technical information about the model
• DeepSeek-V2 Repository - Source code and model weights
• DeepSeek 67B Paper - Information about the predecessor model
• DeepSeekMoE Paper - Details on the MoE architecture
• Model License - Usage terms and conditions
• Code License (MIT) - Source code licensing details
• arXiv Paper - Research paper describing DeepSeek-V2.5