Summary of Shakti: a 2.5 Billion Parameter Small Language Model Optimized For Edge Ai and Low-resource Environments, by Syed Abdul Gaffar Shakhadri et al.
SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments
by Syed Abdul Gaffar Shakhadri, Kruthika KR, Rakshit Aralimatti
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Shakti is a large-scale language model designed to run efficiently on resource-constrained devices like smartphones and IoT systems. This 2.5 billion parameter model combines high-performance natural language processing with optimized precision and memory usage, making it suitable for real-time AI applications where computational resources are limited. Shakti supports vernacular languages and domain-specific tasks, excelling in industries such as healthcare, finance, and customer service. Benchmark evaluations show that Shakti performs competitively against larger models while maintaining low latency and on-device efficiency, positioning it as a leading solution for edge AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Shakti is a new kind of computer model that can do lots of things with words. It’s special because it was made to work well even when computers are limited or slow. This means Shakti can be used in places like smartphones and smart devices that need to process information quickly. The model is good at understanding many different languages and can help with specific tasks, like helping doctors or financial experts make decisions. Overall, Shakti is a useful tool for making AI happen on devices where it might not have been possible before. |
Keywords
* Artificial intelligence * Language model * Natural language processing * Precision
