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

About

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.

Syed Abdul Gaffar Shakhadri, Kruthika KR, Rakshit Aralimatti• 2024

Related benchmarks

TaskDatasetResultRank
Multi-task Language UnderstandingMMLU
Accuracy71.7
842
ReasoningPIQA
Accuracy86.2
133
Truthful QATruthful QA
Accuracy68.4
83
Trivia QATrivia QA
Accuracy58.2
32
Factual KnowledgeBool Q
Accuracy61.1
26
Medical KnowledgeMedQA
Accuracy60.3
20
Commonsense ReasoningBigBenchHard
Accuracy58.2
18
Downstream TaskHellaSwag
Accuracy52.4
13
Social UnderstandingSocial QA
Accuracy79.2
6
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