This work presents a system for on-device text simplification that enables users to process sensitive text without relying on cloud-based services. Through the use of quantization techniques and a novel approach to controllable text simplification, we reduce model size by up to 75% with minimal performance degradation. Our models demonstrate efficient state-of-the-art results using a synthetic dataset of 2,909 examples, outperforming prior work trained on 300K examples. This efficiency stems from: (1) a single control token strategy that precisely targets specific reading levels, (2) a contrastive training approach that enriches model understanding through exposure to multiple simplification levels, and (3) individual models that dedicate full parameter capacity to specific reading level transformations. Our best models achieve up to 82.18 BLEU (at the Advanced level) and 46.12 SARI (at the Elementary level) on standard benchmarks, with performance preserved even after aggressive quantization. This work is implemented as a collaboration with the Mozilla AI team to processes text entirely locally, ensuring sensitive information never leaves the user's device. We have a demonstration video (https://youtu.be/TzmaxnARMzg) and a web demo available at: (https://pablorom2004.github.io/Simplification-Web-Demo)