Here are some common hyperparameters in large language models:

1. Learning rate : This hyperparameter determines how fast the model should update its parameters in response to the loss gradient. A low learning rate can result in slow convergence, while a high learning rate can result in instability and oscillations during training.

2. Number of layers : This hyperparameter determines how many layers the model should have. A larger number of layers can allow the model to learn more complex features, but can also increase the risk of overfitting.

3. Hidden layer size : This hyperparameter determines how many neurons should be in each layer of the model. A larger hidden layer size can allow the model to learn more complex features, but can also increase the risk of overfitting. 4. Dropout rate : This hyperparameter determines the probability of randomly dropping out neurons during training. Dropout can be used to prevent overfitting, but too high a dropout rate can lead to underfitting.

5. Batch size : This hyperparameter determines how many training examples should be used in each batch during training. A larger batch size can lead to faster training, but can also result in less accurate updates.

6. Number of epochs : This hyperparameter determines how many times the model should iterate over the entire training set. Training for too few epochs can result in underfitting, while training for too many epochs can result in overfitting.

7. Regularization strength : This hyperparameter determines how much the model should penalize large weights. Regularization can be used to prevent overfitting, but too strong a regularization can result in underfitting.