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Demystifying DeepSeek
Decoder
Small language models are AI models designed to generate responses to user prompts, just like their much larger cousins. Unlike large language models (LLMs), however, small language models use fewer computational resources, making them ideal for tasks that require speed and efficiency.Ìý
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They particularly excel in specific, focused applications, offering faster response times and reduced energy consumption. This makes them valuable for businesses needing quick, precise AI solutions, especially in environments with limited resources or on mobile devices.ÌýÌý
What are they?
AI models, trained on smaller datasets, for faster, resource-light natural language processing tasks.
What’s in it for you?
Faster processing, lower costs, reduced energy use and efficient deployment on resource-limited devices for targeted AI tasks.
What are the trade-offs?
Limited general knowledge, potentially lower accuracy on complex tasks and less nuanced language generation compared to larger language models.
How are they being used?
They're used for chatbots, targeted information retrieval, on-device processing and streamlining specific language tasks in resource-constrained environments.
What are small language models?
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LLMs may have captured the business world’s imagination but they’re expensive and computationally hungry. Small language models, by contrast, do a similar job but on a smaller and much more efficient scale.Ìý
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This efficiency is crucial for businesses that want to run AI models inside, for example, devices or embed them in systems.
What’s are the benefits of small language models?
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Small language models offer businesses significant advantages in terms of efficiency and cost-effectiveness. They require fewer computational resources, which translates to lower operational costs and reduced energy consumption.ÌýÌý
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Small language models are suited to specific, targeted tasks, like providing responses to domain specific questions, drafting emails When used in a given application they offer faster response times and help improve overall user experience compared to the use of more heavyweight and computationally intensive LLMs.Ìý
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Although like LLMs they can be used for chatbots and other information retrieval applications, their small size can make it easier to integrate them into existing systems.
What are the trade-offs?
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While small language models offer efficiency, they lack the broad general knowledge of larger models. This limits their versatility for tackling complex, open-ended queries.ÌýÌýÌý
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Accuracy on intricate tasks can also sometimes be lower compared to their larger counterparts, which means tasks need to be well-defined and selected appropriately at a development and design stage. Small language models also generate less nuanced language — this can have a potentially negative impact on customer interactions in particularly sensitive applications.
How are small language models being used?
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Small language models are being used in resource-constrained environments, like small devices and hardware at the edge of a network. It might be used, for instance in devices like health trackers or mobile phones or apps. In this consumer context they are also useful as they have additional privacy benefits — the consumer data that interacts with a model can remain at the edge, it doesn’t need to be transferred to a central location.
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This is particularly helpful for things like summarization or customer support, especially if information is specific and well-defined. It means users can get the outputs they need faster and at less cost to the business.
