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Gemini - A Family of Highly Capable Multimodal Models: Evaluation

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Manage episode 391559135 series 3474148
محتوای ارائه شده توسط HackerNoon. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط HackerNoon یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

This story was originally published on HackerNoon at: https://hackernoon.com/gemini-a-family-of-highly-capable-multimodal-models-evaluation.
Gemini - A Family of Highly Capable Multimodal Models: Evaluation
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #generative-ai, #machine-learning, #gemini-model-evaluation, #gemini-family-of-models, #google-gemini, #multimodal-llm, #gemini-pro-vs-gemini-ultra, #gemini-modality-combination, and more.
This story was written by: @escholar. Learn more about this writer by checking @escholar's about page, and for more stories, please visit hackernoon.com.
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks — notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of Gemini models in cross-modal reasoning and language understanding will enable a wide variety of use cases and we discuss our approach toward deploying them responsibly to users.

  continue reading

316 قسمت

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iconاشتراک گذاری
 
Manage episode 391559135 series 3474148
محتوای ارائه شده توسط HackerNoon. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط HackerNoon یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

This story was originally published on HackerNoon at: https://hackernoon.com/gemini-a-family-of-highly-capable-multimodal-models-evaluation.
Gemini - A Family of Highly Capable Multimodal Models: Evaluation
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #generative-ai, #machine-learning, #gemini-model-evaluation, #gemini-family-of-models, #google-gemini, #multimodal-llm, #gemini-pro-vs-gemini-ultra, #gemini-modality-combination, and more.
This story was written by: @escholar. Learn more about this writer by checking @escholar's about page, and for more stories, please visit hackernoon.com.
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks — notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of Gemini models in cross-modal reasoning and language understanding will enable a wide variety of use cases and we discuss our approach toward deploying them responsibly to users.

  continue reading

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