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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Multi-task Language Understanding | MMLU | Accuracy71.7 | 842 | |
| Reasoning | PIQA | Accuracy86.2 | 133 | |
| Truthful QA | Truthful QA | Accuracy68.4 | 83 | |
| Trivia QA | Trivia QA | Accuracy58.2 | 32 | |
| Factual Knowledge | Bool Q | Accuracy61.1 | 26 | |
| Medical Knowledge | MedQA | Accuracy60.3 | 20 | |
| Commonsense Reasoning | BigBenchHard | Accuracy58.2 | 18 | |
| Downstream Task | HellaSwag | Accuracy52.4 | 13 | |
| Social Understanding | Social QA | Accuracy79.2 | 6 |
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