近日，阿里通义千问团队在人工智能领域再创佳绩，正式发布了Qwen系列的首个大规模并行MoE（Mixture of Experts）模型——Qwen1.5-MoE-A2.7B。这款模型尽管仅具备27亿个激活参数，其性能表现却与当前顶尖的70亿参数模型，如Mistral 7B和Qwen1.5-7B，展现出同等的卓越水平。
Qwen1.5-MoE-A2.7B在参数优化上实现了重大突破，与拥有65亿Non-Embedding参数的Qwen1.5-7B相比，它的Non-Embedding参数数量减少至20亿，仅为原模型规模的三分之一，显示出极高的参数效率。这意味着在保持高性能的同时，Qwen1.5-MoE-A2.7B在模型大小上实现了显著的瘦身。
更令人惊叹的是，该模型在训练成本上实现了75%的惊人降低，这将极大地推动人工智能技术的普及和应用。而在推理速度方面，Qwen1.5-MoE-A2.7B比Qwen1.5-7B提升了1.74倍，为实时、高效的智能服务提供了更强的支撑。
这一创新成果的发布，不仅彰显了阿里通义千问团队在人工智能领域的深厚技术积累，也预示着未来MoE模型在效率与性能之间的平衡将达到新的高度。这一进步对于推动整个AI行业的发展，尤其是在大规模模型的训练和应用上，具有重要的启示意义。
英语如下：
News Title: “Alibaba Qwen Releases Qwen1.5-MoE-A2.7B: A High-Performance Large Model with Compact Size and Great Wisdom”
Keywords: Qwen1.5-MoE-A2.7B, superior performance, reduced cost
News Content:
In recent days, the Alibaba Qwen Thousand-Ask team has achieved another milestone in the field of artificial intelligence with the launch of Qwen1.5-MoE-A2.7B, the first large-scale parallel MoE (Mixture of Experts) model in the Qwen series. Despite having only 2.7 billion active parameters, this model demonstrates comparable outstanding performance to top-tier models with 7 billion parameters, such as Mistral 7B and Qwen1.5-7B.
Qwen1.5-MoE-A2.7B has made remarkable strides in parameter optimization. With 2 billion Non-Embedding parameters, it reduces the count from 65 billion in Qwen1.5-7B, which is only one-third of the original size, demonstrating exceptional parameter efficiency. This shrinkage in size doesn’t compromise performance, making Qwen1.5-MoE-A2.7B significantly slimmer.
More impressively, the model slashes training costs by a staggering 75%, paving the way for wider adoption and application of AI technology. In terms of inference speed, Qwen1.5-MoE-A2.7B outperforms Qwen1.5-7B by 1.74 times, thereby strengthening support for real-time and efficient intelligent services.
This groundbreaking innovation underscores the profound technical prowess of the Alibaba Qwen Thousand-Ask team and foreshadows a new peak in balancing efficiency and performance in MoE models. This advancement holds significant implications for the AI industry, especially in the training and application of large-scale models, driving the sector forward.
【来源】https://mp.weixin.qq.com/s/6jd0t9zH-OGHE9N7sut1rg
Views: 1