In an era where Artificial Intelligence is transforming industries, the choice between Large Language Models (LLMs) and Small Language Models (SLMs) plays a pivotal role. Although this decision is mainly driven by the complexity of the task and available resources, there are many other considerations in play when it comes to this very important decision. As of today, HuggingFace hosts over 120,000 models which are available to download. In this post, we hope to offer some insights if you are embarking on the journey of Generative AI adoption.
LLMs, with their staggering number of parameters, are suited for complex tasks requiring deep understanding and context. Models like Falcon-180B and Grok-1 have demonstrated strong capabilities of LLMs in natural language understanding (NLU) and generation (NLG). However, the high computational power, memory, and budget demands make them a challenging choice for running locally. Even running them in your own cloud platform can be very costly. On the flip side, SLMs, typically having fewer parameters, are a practical alternative for tasks generating short text snippets or when resources are constrained. Models like TinyLlama-1.1B have shown that efficiency and cost-effectiveness don't necessarily compromise on performance. There are also some mid-sized models such as Mistral-7B prove to be very effective.
Although performance are important, when it comes to build your own Generative AI capabilities, we must carefully consider the data sensitivity. As business owners or enterprise leaders, we are obligated to keep our customer and client data safe and secure. This means we must always follow the data, be it for training or fine tuning purposes. The UK ICO offers detailed guidance on a number of topics such as:
Another emerging trend is the development of multimodal AI, which not only handles text but also interprets and generates other data formats like images and audio. While this brings new opportunities, it also adds to the complexity and resource demands, making the choice between LLMs and SLMs even more complex and challenging. We hope to offer more insights in future blog posts.
As you can see choosing between LLMs and SLMs is about balancing task requirements and resource constraints as well as your data sensitivity and responsibility. The key is to understand the task at hand, assess the computational power, memory, and budget available, and then choose the model that provides the best performance within those constraints. Model selection is a nuanced process depending on your specific requirements. If you are interested in having a more in-depth discussion with us, please feel free to contact us or leave a comment below.
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