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Mianbi Intelligence and Tsinghua University Unveil China’s First 1.58-bit Large Model BitCPM-CANN
Mingshi Intelligence, in partnership with Tsinghua University and the OpenBMB community, has officially open-sourced BitCPM-CANN, China’s first large language model trained on Huawei’s Ascend platform. Utilizing ternary (1.58-bit) quantization, this breakthrough advances low-bit training technology and marks a significant milestone for China’s AI ecosystem.
BitCPM-CANN showcases the robust capabilities of domestic computing infrastructure, establishing a fully native development pipeline from quantization operators to training algorithms. Available in 0.5B, 1B, 3B, and 8B variants, it outperforms full-precision MiniCPM4 models of equivalent size. Notably, it reduces inference memory usage by approximately sixfold, enabling smooth operation of 8B parameter models on mainstream flagship smartphones and offering substantial benefits to the mobile industry.
Powered by MindSpeed and Megatron-LM, Mingshi Intelligence has constructed a comprehensive low-bit training framework. This infrastructure supports environment adaptation, 32K long-sequence handling, parallel strategies, and integrated operators. By providing this public foundation for all Ascend-based low-bit training, the company lowers development barriers and accelerates technological iteration.
To foster broader adoption, all BitCPM-CANN model weights are open-sourced and accessible via HuggingFace and ModelScope. This initiative equips developers with powerful tools, encouraging innovative applications across the AI landscape.
In conclusion, BitCPM-CANN represents a crucial advancement in China’s AI large model training capabilities, laying a strong foundation for future intelligent applications.
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Mingshi Intelligence, in partnership with Tsinghua University and the OpenBMB community, has officially open-sourced BitCPM-CANN, China’s first large language model trained on Huawei’s Ascend platform. Utilizing ternary (1.58-bit) quantization, this breakthrough advances low-bit training technology and marks a significant milestone for China’s AI ecosystem.
BitCPM-CANN showcases the robust capabilities of domestic computing infrastructure, establishing a fully native development pipeline from quantization operators to training algorithms. Available in 0.5B, 1B, 3B, and 8B variants, it outperforms full-precision MiniCPM4 models of equivalent size. Notably, it reduces inference memory usage by approximately sixfold, enabling smooth operation of 8B parameter models on mainstream flagship smartphones and offering substantial benefits to the mobile industry.
Powered by MindSpeed and Megatron-LM, Mingshi Intelligence has constructed a comprehensive low-bit training framework. This infrastructure supports environment adaptation, 32K long-sequence handling, parallel strategies, and integrated operators. By providing this public foundation for all Ascend-based low-bit training, the company lowers development barriers and accelerates technological iteration.
To foster broader adoption, all BitCPM-CANN model weights are open-sourced and accessible via HuggingFace and ModelScope. This initiative equips developers with powerful tools, encouraging innovative applications across the AI landscape.
In conclusion, BitCPM-CANN represents a crucial advancement in China’s AI large model training capabilities, laying a strong foundation for future intelligent applications.
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