Future Directions

Future directions for SLMs include:

1. More efficient pruning techniques: Developing more efficient pruning techniques to reduce the size and computational requirements of SLMs.
2. More advanced quantization techniques: Developing more advanced quantization techniques to reduce the size and computational requirements of SLMs.
3. More effective knowledge distillation techniques: Developing more effective knowledge distillation techniques to transfer knowledge from larger models to smaller models.
4. More efficient fine-tuning techniques: Developing more efficient fine-tuning techniques to adapt smaller models to specific tasks or datasets.