Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI
  Perspective
Authors: name: Rakshit Aralimatti, name: Syed Abdul Gaffar Shakhadri, name: Kruthika KR, name: Kartik Basavaraj Angadi

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.