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Researchers from DeepSeek-AI and Peking University have introduced a novel strategy called Loss-Free Balancing for Mixture-of-Experts (MoE) models. This approach eliminates the need for auxiliary loss by dynamically adjusting expert biases, ensuring optimal load distribution. The strategy is particularly effective for models with 1B-3B parameters, enhancing performance across 100B-200B tokens. Additionally, a new MoE architecture named Nexus has been developed, focusing on efficiency, specialization, and adaptability. Nexus activates only 30-40% of the model's parameters, making it run up to 1.86 times faster than similar dense models like Mistral-7B and between 1.50 and 1.71 times faster than comparable MoEs such as DeepSeekMoE-16B.
Loss-Free Balancing: A Novel Strategy for Achieving Optimal Load Distribution in Mixture-of-Experts Models with 1B-3B Parameters, Enhancing Performance Across 100B-200B Tokens https://t.co/Wy46n6C6PT #MixtureOfExperts #LoadBalancing #AIIntegration #BusinessTransformation #Enh… https://t.co/13Xo99v9TU
Loss-Free Balancing: A Novel Strategy for Achieving Optimal Load Distribution in Mixture-of-Experts Models with 1B-3B Parameters, Enhancing Performance Across 100B-200B Tokens DeepSeek-AI and Peking University researchers have developed a novel approach called Loss-Free… https://t.co/1Y7mBl8se2
What strategies are behind @deepseek_ai's most powerful DeepSeek-V2 and DeepSeek-Coder-V2 models? DeepSeekMoE is an innovative version of Mixture-of-Experts (MoE) architecture. It enhances the efficiency of DeepSeek LLMs, especially when handling larger datasets and long… https://t.co/zjhDinmgqa
