Customizing the vertical industry's own large language model requires the following steps:
Data collection:
Data is the basis for training a large language model, and a large amount of representative data needs to be collected in order to provide training data for the model. Data can be obtained through web crawlers, manual annotation, etc.
Clean and label data:
For the collected data, it needs to be cleaned and labeled. Cleaning data refers to cleaning and filtering elements such as noise, repetition, errors, and irrelevant elements in the data. At the same time, it is also necessary to label the data, labeling the category, semantics and content of each sample, so as to provide meaningful training data for the model.
Design model structure: After data cleaning and labeling, the model structure needs to be designed. The design of the model structure mainly includes the selection of the appropriate type of large model, the structure of the model, the framework of the model and so on.
Train the model:
After the model architecture is designed, the model needs to be trained. When training a model, it is necessary to select an appropriate training strategy based on the quality and quantity of data, including learning rate adjustment, batch size, regularization, and optimizer.
Model tuning:
After the training is over, the model needs to be tuned. The purpose of tuning is to improve the performance and accuracy of the model. It is necessary to evaluate the performance of the training data set and the test data set, as well as test the model and apply it to actual tasks.
Model integration and application:
After data cleaning, model design, training, and tuning, it is finally necessary to integrate the established large language model into its own vertical industry application and apply it to actual business, thereby improving the efficiency and quality of business.
It should be noted that customizing the large language model of a vertical industry requires certain professional skills in the field of machine learning and natural language processing, including Python programming, deep learning algorithms, and natural language processing technology. In addition, a large amount of data resources and sufficient time and resources are required for model training and adjustment.
