View source for Huawei technologies co., ltd. (20240127000). METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS simplified abstract

=METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS=


==Organization Name==

[[:Category:huawei technologies co., ltd.|huawei technologies co., ltd.]]


[[Category:huawei technologies co., ltd.]]

==Inventor(s)==

[[:Category:Yichun Yin of Shenzhen (CN)|Yichun Yin of Shenzhen (CN)]][[Category:Yichun Yin of Shenzhen (CN)]]

[[:Category:Lifeng Shang of Hong Kong (CN)|Lifeng Shang of Hong Kong (CN)]][[Category:Lifeng Shang of Hong Kong (CN)]]

[[:Category:Cheng Chen of Shenzhen (CN)|Cheng Chen of Shenzhen (CN)]][[Category:Cheng Chen of Shenzhen (CN)]]

[[:Category:Xin Jiang of Hong Kong (CN)|Xin Jiang of Hong Kong (CN)]][[Category:Xin Jiang of Hong Kong (CN)]]

[[:Category:Xiao Chen of Hong Kong (CN)|Xiao Chen of Hong Kong (CN)]][[Category:Xiao Chen of Hong Kong (CN)]]

[[:Category:Qun Liu of Hong Kong (CN)|Qun Liu of Hong Kong (CN)]][[Category:Qun Liu of Hong Kong (CN)]]

==METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240127000 titled 'METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS

==Simplified Explanation==

The abstract describes a computer-implemented method for model training, involving determining weights, forming matrices, initializing a target model, and training the model.

* The method involves determining a set of weights based on matrices associated with source and target models.
* The target model is initialized based on the weights and matrices.
* The target model is then trained using the initialized parameters.

== Potential Applications ==
This technology could be applied in various fields such as machine learning, artificial intelligence, and data analysis for model training and optimization.

== Problems Solved ==
This technology helps in improving the efficiency and accuracy of model training by utilizing weights and matrices to initialize and train the target model effectively.

== Benefits ==
The benefits of this technology include faster model training, improved model performance, and enhanced predictive capabilities in various applications.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in developing advanced predictive models for industries such as finance, healthcare, and marketing.

== Possible Prior Art ==
Prior art in this field may include existing methods for model training and optimization using weights and matrices in machine learning and artificial intelligence research.

=== Unanswered Questions ===

== How does this method compare to traditional model training techniques? ==
The article does not provide a direct comparison to traditional model training techniques using weights and matrices. It would be helpful to understand the specific advantages or differences of this method compared to traditional approaches.

== What are the specific industries or sectors that could benefit most from this technology? ==
The article does not specify the industries or sectors that could benefit most from this technology. It would be valuable to explore the potential applications and impact of this method in different fields.


==Original Abstract Submitted==

a computer-implemented method is provided for model training performed by a processing system. the method comprises determining a set of first weights based on a first matrix associated with a source model, determining a set of second weights based on the set of first weights, forming a second matrix associated with a target model based on the set of first weights and the set of second weights, initializing the target model based on the second matrix, and training the target model.



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