Large Language Models (LLMs) are impressive, but their massive size makes them expensive to run and difficult to deploy on everyday devices. What if we could shrink these behemoths without sacrificing their intelligence? New research introduces "Lillama," a clever compression technique that uses "low-rank feature distillation" to slim down LLMs significantly. Imagine training a massive LLM like Mixtral 8x7B, which normally requires enormous computing power, in mere minutes on a single GPU! Lillama makes this possible. It works by targeting the core building blocks of LLMs, called weight matrices, and approximating them with smaller, more efficient versions. Instead of focusing solely on the model's weights like traditional pruning methods, Lillama focuses on distilling the *activations* of the model. Activations are essentially the intermediate outputs of a model as it processes data. The researchers found that these activations are inherently "low-rank," meaning they contain redundancies that can be exploited for compression. By cleverly combining this insight with a technique called Singular Value Decomposition (SVD) and a novel joint loss function, Lillama speeds up training convergence dramatically. The results are impressive. Experiments show that Lillama can compress Mixtral 8x7B by 20%, saving memory and speeding up processing, while retaining over 95% of its original performance. Even more remarkably, smaller LLMs like Phi-2 3B can be shrunk by a whopping 40% while maintaining competitive performance against similarly sized models. Lillama's efficiency is game-changing, enabling LLMs to run on devices with limited resources, paving the way for wider adoption in various applications. This method is not just limited to transformers; experiments show it effectively compresses Mamba-based LLMs as well. While Lillama offers a promising solution, there are challenges ahead. Integrating it with other compression techniques like quantization and exploring its impact on continued pre-training are crucial next steps. Lillama marks a significant leap toward democratizing access to powerful AI, making it more efficient, affordable, and accessible.