The batch size, learning rate, and number of epochs can all affect the model's performance on a smaller dataset. Here are some general guidelines that you can use as a starting point:

Batch size: A smaller batch size can be more appropriate for smaller datasets because it allows the model to make updates based on more diverse data. For example, a batch size of 32 or 64 is a good starting point for a smaller dataset.

Learning rate: The learning rate determines how fast the model updates its weights. A higher learning rate can allow the model to make rapid progress at the beginning of training, but it can also make the model more prone to overfitting. A lower learning rate can make the model's progress slower, but it can also help the model to generalize better to new data. A learning rate in the range of 0.001 to 0.01 is a good starting point for a smaller dataset.

Number of epochs: The number of epochs is the number of times the model sees the entire dataset during training. A smaller dataset may require fewer epochs to prevent overfitting. For example, you may want to start with a small number of epochs (e.g., 10 or 20) and increase it if the model's performance on the validation set is still improving.

Keep in mind that these are just general guidelines, and the optimal batch size, learning rate, and number of epochs will depend on the specific characteristics of your dataset and model. It may be helpful to experiment with different combinations of these hyperparameters to find the best settings for your particular case.