Preeti
Large Language Models (LLMs) have revolutionized productivity in various tasks, but they come with the challenge of consuming significant computing power. This usually involves a client-server model where the client sends a request to an inferencing server hosting the language models. However, this model requires a good internet connection, subscription fees for maintaining GPUs, and sharing contextual data with the server.
To address these challenges, Small Language Models (SLMs) have emerged as smaller, cost-effective alternatives to LLMs. SLMs utilize optimization techniques like Knowledge Distillation, Pruning, and Quantization to run efficiently on lower compute costs. While not as powerful as LLMs, SLMs still offer a good portion of their capabilities.
SLMs are ideal for performing inference on edge devices, enhancing user privacy, and leveraging the dedicated GPUs or NPUs found in modern devices. With advancements in SLMs and Web GPU technology, it is becoming easier to run these models efficiently on user devices.
By using SLMs and Web GPU, developers can perform inference tasks effectively without overwhelming the main UI thread. Utilizing web workers for heavy lifting and event-driven communication patterns, developers can create interactive applications powered by SLMs.
Overall, SLMs offer a promising solution for running efficient language models on consumer devices, improving user experience and privacy. This shift towards smaller, more optimized models opens up new possibilities for developers to leverage advanced AI capabilities without the need for extensive computing resources.
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Source link: https://codezen.medium.com/unleashing-the-power-of-self-learning-machines-running-slms-on-your-browser-2ed3f3a3496e?source=rss——artificial_intelligence-5
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