The Development of Terrestrial Ecosystems Emerging after Glacier Retreat
Nature(2024)
Univ Milan | INRAE | Univ Montpellier | Lincoln Univ | Univ Los Lagos | Inst Hidrol Meteorol Estudios & Ambientales IDEAM | Cent Univ Punjab | Jawaharlal Nehru Univ | Simon Fraser Univ | Area Evaluac Glaciares & Lagunas Autor Nacl Agua | Univ Paul Valery Montpellier 3 | Univ Grenoble Alpes | Manaaki Whenua Landcare Res Soils & Landscapes | Kyrgyz Natl Acad Sci | Univ Milano Bicocca | Univ Savoie Mont Blanc | MUSE Sci Museum | Norwegian Univ Life Sci | Univ Innsbruck | Herbario Nacl Bolivia La Paz | Univ Veracruzana | Univ Andes | Securing Antarctica's Environmental Future | Chinese Acad Sci | Univ Texas Austin | CNR Inst Water Res
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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