Test Results and Prospects for RD53A, a Large Scale 65 Nm CMOS Chip for Pixel Readout at the HL-LHC
Ist Nazl Fis Nucl | Aix Marseille Univ | LPNHE | LPSC | LAPP | Rheinische Friedrich Wilhelms Univ Bonn | Fachhsch Dortmund | Politecn Bari | INFN Pisa | Natl Inst Subat Phys NIKHEF | Czech Tech Univ | Acad Sci Czech Republ | Sevilla Univ | CSIC UC | European Org Nucl Res CERN | Rutherford Appleton Lab | Fermilab Natl Accelerator Lab | Lawrence Berkeley Natl Lab | Univ New Mexico
- 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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