Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI Perspective
SyedAbdul
Abstract
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.
Community
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 head-on. 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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This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI (2025)
- Vision-Language Models for Edge Networks: A Comprehensive Survey (2025)
- Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing (2025)
- UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings (2025)
- Vision Language Models in Medicine (2025)
- WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale (2025)
- Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models (2025)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
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recommend
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