Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI
  Perspective
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.
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Rakshit Aralimatti
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2025-03-06T10:48:42.715Z
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Rakshit Aralimatti
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RakshitAralimatti
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Syed Abdul Gaffar Shakhadri
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2025-03-06T10:50:31.321Z
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Syed Abdul Gaffar Shakhadri
user
SyedAbdul
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Kruthika KR
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2025-03-06T10:48:42.715Z
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Kruthika
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kruthika
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Kartik Basavaraj Angadi
extracted_pending
2025-03-06T10:48:42.715Z
677cc34fe4cf361eedccd085
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Kartik Basavaraj Angadi
user
KartikAngadi
Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI
  Perspective
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.
Shakti Small Language Models address computational and privacy challenges in deploying large language models on edge devices through efficient architecture, quantization, and fine-tuning.
efficient architectures quantization techniques on-device intelligence smartphones smart appliances IoT systems MMLU Hellaswag compact models fine-tuned edge-AI scenarios