On-device large model optimization
Leveraging hybrid quantization and forward prediction techniques, this technology reduces the size of large models while accelerating inference speed on devices.
How it works
+ Hybrid quantization: Customized quantization solutions are designed based on the characteristics of different parts of the model, forming a hybrid quantization solution that minimizes the model size while maintaining accuracy.
+ Forward prediction: Parallel prediction schemes tailored to specific LLM characteristics enable the system to predict multiple future tokens at once, significantly improving inference speed and efficiency.
User perception
+ Shorter response time + Smaller memory footprint
+ Faster generation + Reduced power consumption
Data security
Technology such as database encryption, sensitive word filtering and large model encryption are used to effectively safeguard user privacy and data security.
How it works
+ Database encryption technology:
advanced encryption technology effectively prevents data from being accessed by unauthorized third parties during the data storage process.
+ Sensitive word filtering technology:
Deep learning-based algorithms extract features from text to automatically detect and block inappropriate content, ensuring compliance and maintaining a safe environment.
+ Large model encryption technology.
User perception
Users perform content processing on the device to effectively safeguard their privacy and data security.