Dhruv Tiwari

Abstract Symmetry protected topological (SPT) phases are fundamental quantum many-body states of matter beyond Landau’s paradigm. Here, we introduce the concept of quantum restoration of SPT (QRSPT) phases, where the protecting symmetry appears to be spontaneously broken at the shortest spatiotemporal scales, but restored after averaging over quantum fluctuations, so that topological features re-emerge.

Originally published on Towards AI. You built an AI Agent that shimmered with promise, a dazzling MVP that aced every demo. It was brilliant in controlled settings. But then, the real world hit. McDonald’s AI drive-thru butchering orders, or self-driving taxis freezing on busy streets? These aren’t minor glitches; they’re stark reminders of the vast chasm between a promising prototype and an AI system capable of handling the sheer chaos of real-life scale.

Dhruv Tiwari · Follow 5 min read · -- Fine-tuning is the process of training a model with specific examples to shape its responses in a desired way. However, it should not be used to teach new knowledge. There are 3 methods to do fine-tuning as of now: Full parameter training Low Rank Adaptation (LoRA) Quantized LoRA (QLoRA) 1. Full Parameter Fine-Tuning Full-parameter fine-tuning is a method of adapting a pre-trained model to a specific task by updating all of its parameters.