Fine-tuning is an application architecture that

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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. 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 Argentina WhatsApp Number 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. We can see that AI is learning from us humans. They are tireless and diligent. We humans should also learn from them,





look at the opinions of people around us without criticism and colored glasses, and use the remaining collective wisdom to continue to lead us to the next step. A new world. In the follow-up, we will conduct an in-depth discussion on the four technical architectures. Welcome to communicate ~ I hope it can bring you some inspiration, please come on. Author Liu Xing Chat Products, public account Liu Xing Chat Products This article was originally published by @ Liu Xing Chat Products on Everyone is a product manager. Reprinting without permission is prohibited. The title picture comes from Unsplash, based on the CC agreement.