Certainly! Here are some less commonly known ways to improve a transformer model's efficiency:

Pruning: This involves removing the unimportant connections in the model, which can significantly reduce the number of parameters and improve the model's speed and memory usage.

Knowledge distillation: This involves training a smaller "student" model to mimic the outputs of a larger "teacher" model, which can result in a more efficient model with similar performance.

Quantization: This involves reducing the precision of the model's weights and activations, which can reduce the model's memory usage and improve inference speed.

Dynamic batching: This involves batching together sequences of different lengths during training, which can reduce padding and improve efficiency.

Adaptive learning rate: This involves adjusting the learning rate based on the gradient magnitude of the model, which can improve convergence speed and reduce the number of training iterations.

I hope these suggestions are helpful!
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