Large Language Models (LLMs) are impressive, but their size makes them resource-intensive to run. What if we could make them smaller and faster without sacrificing much performance? New research explores "depth pruning," a technique for strategically removing parts of an LLM. Imagine a carefully sculpted bonsai tree—smaller than its giant counterpart, but still beautiful and functional. Depth pruning does something similar for LLMs by removing blocks of the model based on their importance. Researchers investigated different ways to measure this importance, including Shapley values, a method that calculates the marginal contribution of each block. They discovered there's a trade-off: improving performance on one task might hurt performance on another. Interestingly, the study found that self-attention layers (responsible for understanding relationships within text) are more prunable than other parts of the model. This is great news for speed because these layers contribute significantly to the computational cost. They even tested simple methods to recover any lost performance after pruning. A simple average update worked surprisingly well, sometimes even improving scores! While there's still work to be done, depth pruning shows real promise for creating leaner, faster LLMs without major compromises.