Implementing Small Language Models (SLMs) with RAG on Embedded Devices Leading to Cost Reduction, Data Privacy, and Offline Use

In today's fast-changing world of generative AI, deepsense.ai is creating new solutions by combining Advanced Retrieval-Augmented Generation (RAG) with Small Language Models (SLMs). SLMs are compact versions of language models with fewer parameters, offering benefits like cost reduction, improved data privacy, and offline functionality. These achievements and ongoing research efforts aim to enhance SLM application on edge devices, despite challenges such as memory limitations and platform independence. By improving support for inference engines, exploring new models, and optimizing performance, deepsense.ai is paving the way for significant growth in the field, especially on mobile devices. For more information, visit https://github.com/deepsense-ai/edge-slm. Implementing Small Language Models (SLMs) with RAG on Embedded Devices In today’s rapidly evolving generative AI world, keeping pace requires more than embracing cutting-edge technology. At deepsense.ai, we don’t merely follow trends; we aspire to establish new solutions. Our latest achievement combines Advanced Retrieval-Augmented Generation (RAG) with Small Language Models (SLMs), aiming to enhance the capabilities of embedded devices beyond traditional cloud solutions. Yet, it’s not solely about the technology – it’s about the business opportunities it presents: cost reduction, improved data privacy, and seamless offline functionality. What are Small Language Models? Small Language Models (SLMs) are smaller counterparts of Large Language Models, with fewer parameters, making them more lightweight and faster in inference time. SLMs excel in two main areas: Benefits of SLMs on Edge Devices Cost Reduction: Transitioning LLM-based solutions directly to edge devices eliminates the need for cloud inference, resulting in significant cost savings at scale. Offline Functionality: Deploying SLMs directly on edge devices eliminates the requirement for internet access, making SLM-based solutions suitable for scenarios where internet connectivity is limited. Data Privacy: All processing occurs locally on the edge, offering the opportunity to adopt Language Model-based solutions while adhering to stringent data protection protocols. Developing a Complete RAG Pipeline with SLMs on a Mobile Phone The main goal of this internal project was to develop a complete Retrieval-Augmented Generation (RAG) pipeline, encompassing the embedding model, retrieval of relevant document chunks, and the question-answering model, ready for deployment on resource-constrained Android devices. Experimenting with SLMs and evaluating their performance on various devices revealed the potential for practical applications of SLMs on edge devices. Challenges and Ongoing Research Key challenges, such as memory limitations and platform independence, influence the implementation of SLMs with RAG on embedded devices. Ongoing research efforts aim to break the current limits of SLMs and further improve their performance and efficiency. Conclusion Running SLM on edge devices and achieving satisfactory results for applications such as RAG is possible, both in terms of speed and quality. However, important caveats need to be considered. We expect rapid advancements in the field, leading to more powerful and efficient SLM solutions. Spotlight on a Practical AI Solution Discover how AI can transform your sales processes and customer engagement with the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. List of Useful Links: AI Lab in Telegram @aiscrumbot – free consultation Implementing Small Language Models (SLMs) with RAG on Embedded Devices Leading to Cost Reduction, Data Privacy, and Offline Use deepsense.ai Twitter – @itinaicom