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The rise of Large Language Models (LS Models) has revolutionized conversational AI, yet their integration into low-latency, real-time voice systems remains challenging. This paper explores the application of LS Models within , a platform designed for high-performance voice agents. We propose an architecture combining Dasha’s event-driven runtime with fine-tuned transformer-based LS Models for intent recognition, dialogue management, and response generation. Experimental results show a 23% improvement in task completion rates and a 40% reduction in response latency compared to traditional ASR+NLU pipelines. Our findings suggest that LS Models, when optimized for streaming inference, significantly enhance naturalness and robustness in voice applications.
LS Models has earned a reputation for "museum-quality" precision, often being the first choice for European railway enthusiasts seeking exact replicas of international passenger coaches and locomotives. ls models dasha
