Imagine fine-tuning powerful AI models like Llama2 right on your phone. It sounds impossible, right? Large language models (LLMs) are usually confined to powerful computers with tons of memory. Fine-tuning them on smaller devices like phones or laptops has been a major challenge. But what if we could achieve the same results by tweaking just a tiny fraction of the model's parameters? That’s the groundbreaking idea behind a new research paper on zeroth-order fine-tuning with extreme sparsity. Researchers have found a way to pinpoint the most "sensitive" parts of an LLM—the parameters that matter most for a specific task. By focusing on these key parameters (as little as 0.1%), they've achieved performance comparable to full fine-tuning, all while using significantly less memory and time. The secret lies in leveraging the pre-training process. LLMs are trained on massive datasets, and this training implicitly identifies parameters crucial for various downstream tasks. This means we can personalize a model on a device by efficiently quantizing the less important parameters to 4 bits and only fine-tuning those few “sensitive” ones. This research has significant implications for on-device personalization of LLMs. It opens up exciting possibilities for creating AI assistants that adapt to individual user preferences and data without needing massive computing power or sending sensitive data to the cloud. While challenges remain in implementing these sparse operations efficiently across different hardware, this research paves the way for a future where powerful AI becomes truly accessible on everyday devices.