Imagine running powerful AI models, like the ones that power sophisticated chatbots and language translators, right on your phone or a small, low-power device. This seemingly impossible feat is getting closer to reality thanks to a new technique called PV-Tuning. The challenge lies in the sheer size of these models. They're so massive that they often don't fit in the memory of ordinary devices. One solution is 'extreme compression,' shrinking the models down to a fraction of their original size. Previous methods have tried various tricks, like clever encoding and fine-tuning parts of the compressed model. However, these techniques are hitting their limits. PV-Tuning takes a different approach. It focuses on how the model is fine-tuned after compression. Instead of making tiny adjustments across the entire model, PV-Tuning makes larger, more impactful changes to a carefully selected subset of the model's parameters. This targeted approach allows for more significant improvements in accuracy, even with extreme compression. The results are impressive. PV-Tuning achieves state-of-the-art performance for compressing large language models like Llama and Mistral, outperforming existing methods in the extreme 1-2 bits-per-parameter compression range. This breakthrough means we can now run these powerful AI models on smaller devices without sacrificing too much performance. While PV-Tuning requires more computational resources during the fine-tuning process, the payoff is significant. It opens doors to a future where powerful AI is accessible on everyday devices, enabling exciting new applications in offline translation, personalized AI assistants, and privacy-preserving on-device AI.