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

15 Oct 2024arXiv:2410.11331archive 2025-07-28

Syed Abdul Gaffar Shakhadri, Kruthika KR, Rakshit Aralimatti

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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Tasks

Language ModelingLanguage ModellingQuestion AnsweringSmall Language Model

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering BBH Shakti-LLM (2.5B) Accuracy 58.2 #1 of 1 Archive leaderboard report
Question Answering BoolQ Shakti-LLM (2.5B) Accuracy 61.1 #54 of 65 Archive leaderboard report
Question Answering HellaSwag Shakti-LLM (2.5B) Accuracy 52.4 #1 of 1 Archive leaderboard report
Question Answering MML qwen-LLM 7B Accuracy 71.8 #1 of 1 Archive leaderboard report
Question Answering MedQA Shakti-LLM (2.5B) Accuracy 60.3 #12 of 27 Archive leaderboard report
Question Answering PIQA Shakti-LLM (2.5B) Accuracy 86.2 #8 of 67 Archive leaderboard report
Question Answering TriviaQA Shakti-LLM (2.5B) EM 58.2 #39 of 56 Archive leaderboard report
Question Answering TruthfulQA Shakti-LLM (2.5B) Accuracy 68.4 #33 of 33 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AttentionAttention DropoutBPELayer NormalizationLinear LayerMulti-Head AttentionResidual ConnectionSoftmax

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