Why You Need To Know About Small Language Models

🌈 Abstract

The article discusses the distinction between large language models (LLMs) and small language models (SLMs), highlighting the advantages and use cases of SLMs. It covers the characteristics of SLMs, their ability to be fine-tuned for specific domains, and their cost-effectiveness compared to LLMs. The article also explores the potential of SLMs to be deployed on edge devices, providing fast and private AI capabilities.

🙋 Q&A

[01] Large Language Models (LLMs) vs. Small Language Models (SLMs)

1. What is the key distinction between LLMs and SLMs?

• LLMs are general-purpose models that cater to a wide range of interests and tasks, similar to a cruise ship offering various activities.
• SLMs are more targeted and efficient, like a specialized boat for fishing or waterskiing, designed for specific tasks or domains.

2. How are the sizes of LLMs and SLMs defined?

• LLMs like GPT-4 are rumored to have trillions of parameters, while SLMs are considered to have fewer than 10 billion parameters.
• Examples of SLMs include Meta's Llama 3.1 (8B), Google's Gemma (7B & 2B), Microsoft's Phi-3 small (7B), and OpenAI's GPT-4 mini (8B).

3. What are the advantages of SLMs over LLMs?

• SLMs can be more easily fine-tuned for niche, domain-specific tasks, as the computing resources required are much lower compared to LLMs.
• SLMs are significantly more cost-effective, with pricing for input and output tokens being much lower than LLMs like GPT-4.

[02] Deployment of SLMs on Edge Devices

1. How can SLMs be used on edge devices?

• SLMs can be deployed on various devices, such as smartphones, smart home devices, wearables, and automotive systems, allowing for fast, private, and local AI processing.
• The idea is to have a local SLM handle most requests, with the ability to route more complex tasks to a larger, more powerful model if needed.

2. What are the benefits of using SLMs on edge devices?

• SLMs on edge devices provide ultra-fast processing speeds due to local processing, as well as enhanced privacy since requests are handled locally without going to the cloud.
• This approach allows for AI features and capabilities to be directly integrated into user devices, providing a rich and seamless user experience.

3. What are the future trends in the deployment of SLMs?

• The future of AI is likely to be centered around the orchestration of millions of SLMs running on edge devices, rather than a single large frontier model.
• This distributed approach with SLMs will enable a wide range of AI-powered features and capabilities to be integrated into various user devices, from wearables to smartphones and beyond.