Characteristics of the model by calling
The internal functions of the model. For example, we can call the model's word vector function to obtain the word vector of a word. The advantage of this method is that it can directly obtain the internal information of the model, but the disadvantage is that it requires an in-depth understanding of the internal structure of the model. The third type of RAGRetrieval-Augmented Generation RAG is an application architecture that combines retrieval and generation. In this approach, the model first retrieves relevant text and then uses this text as input for the model to generate an answer. For example, if we want the model to answer a question about global warming,the model can first retrieve some articles about global warming and then generate an answer based on these articles. The advantage of this method is that it can utilize a large amount of external information to improve the quality of model generation. But the disadvantage Argentina WhatsApp Number is that it requires a lot of computing resources because a large amount of text needs to be retrieved. The fourth type of fine-tuning is fine-tuning. Fine-tuning is an application architecture that further trains the model on specific tasks, such as calculating steel consumption and so on. In this method, the model is first pre-trained on a large amount of text to learn the basic rules of language. Then, the model will be fine-tuned on the data of the specific task to learn the specific rules of the task. For example, we can fine-tune the model on sentiment analysis tasks to allow the model to better understand sentiment.
https://lh7-us.googleusercontent.com/R_4hcuc8DWv2m6wi-1iVdxUkEDekRPGdvBjrnzhrk9RMutLcx9Hu9oklGKgWHMljUA7qVJbMj8f9bMwhOodwv3DzKhh6vj_ViAU4u2Tc9yyWk63v1mG8IuPCTRFg60yvhCvXMq7f41C-FP1QzfYtBVQ
The advantage of this method is that it can improve the performance of the model on specific tasks, but the disadvantage is that it requires a large amount of labeled data. Final words In general, the principle of generating results by the GPT large model is to learn the rules of language and then predict the next word based on the existing context, thereby generating coherent text. This is just like us humans speaking or writing, predicting the next word or phrase based on the existing context. However, the learning and generation capabilities of the GPT model far exceed those of us humans.
頁:
[1]