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Does Generative AI Model stores parameters inside the model

Yes, the parameters learned by a GPT model are stored inside the model itself. 

These parameters include the weights and biases of the neural network layers that make up the model. 

When a GPT model is saved or deployed for use, it typically includes these learned parameters, allowing it to generate text without needing access to the original training data.

The parameters are essential for the model's functionality because they capture the knowledge and patterns learned during the training process. 

When you fine-tune a GPT model for a specific task or domain, you update some of these parameters to adapt the model to that particular application. 

These parameters are then saved along with the model architecture, and the fine-tuned model can be used for generating text relevant to the fine-tuning task.

So, in summary, GPT models store their learned parameters internally, which are crucial for their text generation capabilities. These parameters are used during both the pre-training and fine-tuning stages of the model's development.

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