SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments
This addresses the need for efficient AI in edge computing for industries such as healthcare and finance, though it appears incremental as an optimized version of existing small language models.
The paper tackles the problem of deploying language models in resource-constrained environments like edge devices by introducing Shakti, a 2.5 billion parameter model optimized for efficiency, which performs competitively against larger models while maintaining low latency and on-device efficiency.
We introduce Shakti, a 2.5 billion parameter language model specifically optimized for resource-constrained environments such as edge devices, including smartphones, wearables, and IoT systems. Shakti combines high-performance NLP with optimized efficiency and precision, making it ideal for real-time AI applications where computational resources and memory are limited. With support for vernacular languages and domain-specific tasks, Shakti excels in industries such as healthcare, finance, and customer service. Benchmark evaluations demonstrate that Shakti performs competitively against larger models while maintaining low latency and on-device efficiency, positioning it as a leading solution for edge AI.