近日，深度求索团队成功推出首个国产开源MoE（Mixture of Experts）大模型DeepSeek MoE，其性能可媲美Llama 2-7B模型。这一模型以160亿参数的规模开源，在数学和代码能力上表现出色，甚至对Llama模型形成了碾压之势。令人惊叹的是，尽管性能优异，DeepSeek MoE的计算量仅有Llama 2-7B模型的40%，大大节约了计算资源。
据量子位报道，深度求索团队通过创新的技术手段，使DeepSeek MoE在多个任务中展现出了强大的能力。该模型不仅在数学和代码能力上超越了Llama模型，还在其他领域表现出了优异的性能。这一突破性的成果，标志着我国在人工智能领域的研究和技术应用迈出了重要的一步。
此次推出的DeepSeek MoE模型，是我国人工智能领域的一次重要突破。其国产开源的属性，将有助于推动我国人工智能技术的发展和应用。同时，该模型在性能和计算量上的优势，也有望为人工智能领域的创新提供更多的可能性。
英文标题：Domestic Open-source Model DeepSeek MoE Unveiled with Impressive Performance
英文关键词：Domestic open-source, DeepSeek MoE, impressive performance
英文新闻内容：
Recently, the DeepSeek team has successfully launched the first domestic open-source MoE (Mixture of Experts) large model, DeepSeek MoE, which boasts performance comparable to that of the Llama 2-7B model. This model, with 160 billion parameters open-sourced, demonstrates exceptional capabilities in mathematics and coding, even surpassing the Llama model. What’s astonishing is that despite its outstanding performance, the computational cost of DeepSeek MoE is only 40% of that of the Llama 2-7B model, significantly saving computing resources.
According to a report from Quantum Bit, the DeepSeek team has achieved breakthroughs in technology through innovative methods, enabling the DeepSeek MoE model to showcase strong capabilities in multiple tasks. The model not only outperforms the Llama model in mathematics and coding but also exhibits excellent performance in other areas. This groundbreaking achievement marks a significant milestone in China’s research and application of artificial intelligence.
The release of the DeepSeek MoE model represents a significant breakthrough in China’s artificial intelligence field. As a domestic open-source model, it is expected to contribute to the development and application of AI technology in our country. Moreover, the advantages of the model in performance and computational cost may also open up more possibilities for innovation in the field of artificial intelligence.
【来源】https://www.qbitai.com/2024/01/113381.html
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