Papers › SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and...
SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments
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
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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 |
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Methods
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