Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI Perspective
This addresses computational and privacy issues for edge AI applications like smartphones and IoT, but it is incremental as it builds on existing methods for model efficiency.
The paper tackles the challenges of deploying large language models on edge devices by introducing the Shakti Small Language Models (SLMs) with efficient architectures and quantization, showing that these compact models can meet or exceed expectations in real-world edge-AI scenarios.
Deploying large scale language models on edge devices faces inherent challenges such as high computational demands, energy consumption, and potential data privacy risks. This paper introduces the Shakti Small Language Models (SLMs) Shakti-100M, Shakti-250M, and Shakti-500M which target these constraints headon. By combining efficient architectures, quantization techniques, and responsible AI principles, the Shakti series enables on-device intelligence for smartphones, smart appliances, IoT systems, and beyond. We provide comprehensive insights into their design philosophy, training pipelines, and benchmark performance on both general tasks (e.g., MMLU, Hellaswag) and specialized domains (healthcare, finance, and legal). Our findings illustrate that compact models, when carefully engineered and fine-tuned, can meet and often exceed expectations in real-world edge-AI scenarios.