here are some uncommon ways to improve a transformer model's efficiency:

1. Parameter pruning: This involves removing unnecessary weights from the model to reduce the number of parameters and make the model more efficient.
2. Knowledge distillation: This involves training a smaller model to mimic the behavior of a larger, more complex model, thereby reducing the computational requirements of the model.
3. Mixed precision training: This involves training the model with lower precision (e.g., 16-bit instead of 32-bit), which can reduce memory requirements and computation time.
4. Architecture optimization: This involves modifying the architecture of the transformer model to make it more efficient. For example, using a different attention mechanism or modifying the number of layers in the model.
5. Quantization: This involves representing the weights and activations of the model using fewer bits, which can reduce memory requirements and computation time.
6. Dynamic computation graphs: This involves creating a dynamic computation graph that changes during training, rather than using a static graph, which can improve efficiency.
7. Knowledge reuse: This involves reusing pre-trained models or components of pre-trained models to improve the efficiency of the transformer model.

These are just a few examples of the many ways in which the efficiency of a transformer model can be improved. It's important to note that the best approach will depend on the specific use case and requirements of the model.
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