Finding the right SLM for your needs - a guide to Small Language Models

Recent months have seen a rise in open-source Small Language Models (SLMs) with parameters ranging from 1 to 3 billion, such as Microsoft's Phi-3-mini, Google's Gemma-2, and Meta's Llama-3.2, which are designed for efficient deployment on mobile and edge devices due to their speed and cost-effectiveness. Although these models have fewer parameters than Large Language Models (LLMs), resulting in limited reasoning capabilities, they excel in tasks like summarization and data extraction. SLMs are primarily trained using techniques like pruning and knowledge distillation, with the latter being more efficient. Despite their smaller size, SLMs trained with ample data can achieve performance comparable to LLMs, making them an effective choice for resource-conscious deployments. The performance gap between SLMs and LLMs is narrowing, as evidenced by improved benchmark scores and lower latency in SLMs. However, SLMs require more data to reach similar performance levels and are less suited for tasks demanding extensive reasoning. As SLMs continue to evolve, they are becoming ideal for simple data transformation tasks and edge use cases, offering a balance between performance and efficiency. Solutions like Refuel facilitate the use of SLMs by providing tools for few-shot learning and distillation, enabling users to deploy these models effectively.