Small Language Models

While large language models (LLMs) are booming and discussed everywhere, there’s another important trend in the AI world—Small Language Models (SLMs).

A small language model (SLM) is a language model similar to a large language model but with a significantly reduced number of parameters. SLMs typically range from a few million to a few billion parameters, while LLMs have hundreds of billions or even trillions. For example, GPT-3 has 175 billion parameters, whereas Microsoft’s Phi-2, an SLM, has just 2 billion.

Main techniques to train SLMs:
✏️ Knowledge Distillation. A smaller model (the "student") learns from a bigger, already-trained model (the "teacher"). The student model is trained to not only match the teacher model’s predictions but also mimic its underlying process of reasoning. Typically the teacher model’s weights are frozen and cannot be changed during the distillation process.
✏️ Pruning. This is a process of getting rid of the extra bits that aren't really needed, making it smaller and faster without loosing too much accuracy.

Key advantages:
✔️ Resource Efficiency. SLMs are more compact and require fewer resources that makes them suitable for deployment on small devices.
✔️Cost-Effectiveness. They are much cheaper to train and deploy compared to LLMs.
✔️ Customization. SLMs can be fine-tuned on specific datasets, making them highly efficient for specialized tasks in particular industries.
✔️ Security. SLMs can be deployed locally or in private cloud environments, keeping sensitive data under organizational control.

There’s no one-size-fits-all solution when it comes to AI adoption. Every business will focus on efficiency and select the best and most cost-effective tool to get the job done properly. Architects should carefully select the right-sized model for each project based on its goals and constraints.

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