Abstract We propose DeepSeek-V2, a powerful Mixture of Experts (MoE) language model characterized by economical training and efficient inference. It has a total of 236 billion parameters, with 21 billion parameters activated per token, and supports 128K tokens of context length. DeepSeek-V2 adopts innovative architectures such as Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA ensures … Read more
Following the release of large models for language, code, mathematics, etc., DeepSeek has brought another early achievement on the journey towards AGI… DeepSeekVL, jointly expanding training data, model architecture, and training strategies, attempts to build the strongest open-source 7B and 1.3B multimodal models. Highlights Data: Multi-source multimodal data enhances the model’s general cross-modal capabilities, mixing … Read more
DeepSeek has introduced a new MoE model, DeepSeek-V2, with a total parameter count of 236 billion and 21 billion active parameters. Although it is still a bit short of GPT-4 levels, it can be considered the strongest open-source MoE model available. Staying true to its open-source spirit, the accompanying technical report is also packed with … Read more
Deepseek has recently released the v2 version of its model, continuing the technical route of the Deepseek-MoE (Mixture of Experts) model released in January. It employs a large number of small parameter experts for modeling and incorporates more optimizations in training and inference. True to its tradition, Deepseek has fully open-sourced the model (base and … Read more