25亿参数小模型，专为手机等设备优化，实时运行不卡顿。
SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments
- 25亿参数，针对边缘设备优化，内存和算力要求低。
- 在医疗、金融等领域表现接近大模型，延迟极低。
- 支持本地部署，适合隐私敏感或网络受限场景。
我们提出Shakti，一个25亿参数的语言模型，专为资源受限的边缘设备（如智能手机、可穿戴设备和物联网系统）设计。该模型兼顾高性能自然语言处理与高效能、高精度，适用于计算资源和内存有限的实时AI应用。支持方言语言和特定领域任务，在医疗、金融及客户服务等行业表现优异。基准测试表明，Shakti在性能上可媲美更大模型，同时保持低延迟与设备端运行效率，是边缘AI领域的领先解决方案。
原文摘要 · Abstract (English)
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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