In today’s rapidly evolving generative AI world, deepsense.ai aims to establish 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 seamless offline functionality. The achievements and ongoing research represent efforts to enhance SLM application on edge devices, despite challenges such as memory limitations and platform independence. Efforts are being made to improve inference engine support, explore new models, and optimize performance, thereby setting the stage for significant growth in the field, particularly on mobile devices. For more information, visit: https://github.com/deepsense-ai/edge-slm.
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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?
Inherently, Small Language Models (SLMs) are smaller counterparts of Large Language Models. They have fewer parameters and are more lightweight and faster in inference time. We can consider models with more than 7 billion parameters as LLMs (the largest could have even more than 1 trillion parameters), demanding resource-heavy training and inference. SLMs are compact versions of Language Models, and they excel in two main areas:
Benefits of SLMs on Edge Devices
Here are three compelling reasons why companies may find Small Language Model (SLM) applications preferable to their cloud-heavy Large Language Model (LLM) counterparts:
- 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 by running 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. To gain hands-on experience with Small Language Models, we experimented with SLMs and evaluated their performance on various devices. Our findings revealed the potential for practical applications of SLMs on edge devices.
Challenges and Ongoing Research
We identified key challenges, such as memory limitations and platform independence, that 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
While it is indeed possible to run SLM on edge devices and have satisfactory results for applications such as RAG, both in terms of speed and quality, some important caveats need to be considered. We expect rapid advancements in the field, leading to more powerful and efficient SLM solutions.
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