DeepSeek MoE
About
The DeepSeek MoE family represents a groundbreaking series of large language models that leverage the Mixture-of-Experts (MoE) architecture to optimize computational efficiency and performance. By selectively activating a subset of parameters, known as "experts," these models minimize computational demands while maintaining robust capabilities. They utilize strategies like fine-grained expert segmentation, allowing specialized unit divisions, and shared expert isolation, which reduces redundancy in common knowledge experts. The lineup includes models such as the DeepSeekMoE 16B, which matches the performance of LLaMA2 7B with significantly less computation, and the DeepSeek-V2 family, incorporating Multi-head Latent Attention (MLA) for enhanced inference efficiency. Available on platforms like Hugging Face and GitHub, DeepSeek models span a range from the extensive DeepSeek-V2, with 236 billion parameters, to the more compact DeepSeek-V2-Lite, facilitating open-source research and development.