SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments

Kruthika KR; Rakshit Aralimatti; Syed Abdul Gaffar Shakhadri

SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments

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arxiv 2410.11331 v2 pith:JTBOTRVQ submitted 2024-10-15 cs.CL cs.CVcs.LG

Syed Abdul Gaffar Shakhadri , Kruthika KR , Rakshit Aralimatti This is my paper · ORCID

classification cs.CLcs.CVcs.LG

keywords shaktiedgeoptimizedbillionefficiencyenvironmentslanguagemodel

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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.

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