Large Language Models (LLMs) are impressive, but their multi-head attention mechanism can be resource intensive. Researchers have explored ways to optimize this, like pruning or sharing parameters, but these methods often compromise performance or require costly retraining. A new approach called Decoupled-Head Attention (DHA) offers a clever solution. Imagine the LLM's attention mechanism as having multiple heads, each focusing on different parts of the input text. DHA analyzes these heads and finds that some have very similar functions. Instead of keeping all these redundant heads, DHA merges them, combining their knowledge and processing power. This not only saves memory and computation, but also maintains performance. Unlike previous optimization strategies, DHA can quickly adapt existing LLM models without needing extensive retraining. The key insight is that DHA adaptively shares certain key and value "heads" across different layers of the model. It's like figuring out which information is most valuable and distributing it selectively, leading to a more efficient use of resources. The results? DHA can cut down the memory overhead by 75% and achieve almost the same level of performance as the original model, using only a tiny fraction of the original training data. In fact, it outperforms other similar methods like Grouped-Query Attention, speeding up training by a factor of 5 and delivering better performance. This new research opens doors to more efficient LLMs, potentially making them more accessible and sustainable. While this research primarily focuses on LLMs based on the transformer decoder architecture, like the popular LLaMA model, its principles could be extended to other architectures in the future. There's still room for improvement, such as exploring more sophisticated non-linear merging techniques. Nevertheless, DHA is a significant step forward in optimizing LLMs, promising faster, more efficient, and more accessible AI models in the years to come.