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Google:Gemini 技术报告(英文版)(90页).pdf

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1、Gemini:A Family of Highly CapableMultimodal ModelsGemini Team,Google1This report introduces a new family of multimodal models,Gemini,that exhibit remarkable capabilitiesacross image,audio,video,and text understanding.The Gemini family consists of Ultra,Pro,and Nanosizes,suitable for applications ran

2、ging from complex reasoning tasks to on-device memory-constraineduse-cases.Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra modeladvances the state of the art in 30 of 32 of these benchmarks notably being the first model to achievehuman-expert performance on the wel

3、l-studied exam benchmark MMLU,and improving the state of theart in every one of the 20 multimodal benchmarks we examined.We believe that the new capabilities ofthe Gemini family in cross-modal reasoning and language understanding will enable a wide variety ofuse cases.We discuss our approach toward

4、post-training and deploying Gemini models responsibly tousers through services including Gemini,Gemini Advanced,Google AI Studio,and Cloud Vertex AI.1.IntroductionWe present Gemini,a family of highly capable multimodal models developed at Google.We trainedGemini models jointly across image,audio,vid

5、eo,and text data for the purpose of building a modelwith both strong generalist capabilities across modalities alongside cutting-edge understanding andreasoning performance in each respective domain.Gemini 1.0,our first version,comes in three sizes:Ultra for highly-complex tasks,Pro for enhancedperf

6、ormance and deployability at scale,and Nano for on-device applications.Each size is specificallytailored to address different computational limitations and application requirements.After large-scale pre-training,we post-train our models to improve overall quality,enhance targetcapabilities,and ensur

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本文介绍了Gemini,一个由Google开发的高度多模态模型家族。Gemini家族包括三种大小:Ultra、Pro和Nano,适用于从复杂推理任务到设备内存受限用例的广泛应用。Gemini Ultra模型在32个基准测试中刷新了30个基准测试的记录,包括10个文本和推理基准测试,9个图像理解基准测试,6个视频理解基准测试和5个语音识别及翻译基准测试。Gemini Ultra是第一个在MMLU基准测试中超过人类专家水平的模型,得分为90.04%。Gemini Pro模型在各种任务上表现出色,而Gemini Nano模型则针对设备部署进行了优化。Gemini模型在跨模态推理和语言理解方面的新能力将广泛应用于各种用例。
"Gemini模型如何实现多模态理解?" "Gemini模型在哪些方面超越了现有模型?" "Gemini模型如何应用于教育和学习领域?"
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