title: Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI
    Perspective
summary: 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.
paper_authors._id: 67c97d89ac48d4b9ebf73f21
paper_authors.name: Rakshit Aralimatti
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paper_authors.fullname: Rakshit Aralimatti
paper_authors.type: user
paper_authors.user: RakshitAralimatti
paper_authors._id: 67c97d89ac48d4b9ebf73f22
paper_authors.name: Syed Abdul Gaffar Shakhadri
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paper_authors.fullname: Syed Abdul Gaffar Shakhadri
paper_authors.type: user
paper_authors.user: SyedAbdul
paper_authors._id: 67c97d89ac48d4b9ebf73f23
paper_authors.name: Kruthika KR
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-06T10:48:42.715Z
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paper_authors.fullname: Kruthika
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paper_authors._id: 67c97d89ac48d4b9ebf73f24
paper_authors.name: Kartik Basavaraj Angadi
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-06T10:48:42.715Z
paper_authors._id: 677cc34fe4cf361eedccd085
paper_authors.avatarUrl: /avatars/e97a3f9a84ed258ab4b75c12865562d6.svg
paper_authors.fullname: Kartik Basavaraj Angadi
paper_authors.type: user
paper_authors.user: KartikAngadi
paper_title: Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI
    Perspective
paper_summary: 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.
paper_ai_keywords: efficient architectures quantization techniques responsible AI principles on-device intelligence smartphones smart appliances IoT systems benchmark performance MMLU Hellaswag healthcare finance legal compact models fine-tuned edge-AI scenarios