Generative AI has been gaining huge traction recently thanks to its ability to autonomously generate high-quality text, images, audio and other forms of content. It has various applications in different domains, from content creation and marketing to healthcare, software development and finance. Applications powered by generative AI models can automate tedious and repetitive tasks in a business environment, showcasing intelligent decision-making skills. Whether chatbots, virtual assistants, or predictive analytics apps, generative AI revolutionizes businesses’ operations.
It is, however, challenging to create models that can produce output that is both coherent and contextually relevant in generative AI applications. Pre-trained models emerge as a powerful solution to this issue. Because they are trained on massive amounts of data, pre-trained language models can generate text similar to human language. But there may be situations where pre-trained models do not perform optimally for a particular application or domain. A pre-trained model needs to be fine-tuned in this situation.
To read more – https://www.leewayhertz.com/fine-tuning-pre-trained-models/
Matthew Haws 