Small language models (SLMs) are gaining popularity, driven by the need for privacy and user ownership. In this session you will learn what large language models (LLMs) are, how they internally work and the factors that led to their rise. We will provide a brief overview of AI's history, highlighting the development of Transformers and generative models. Next, we’ll introduce SLMs and explain the importance of ensuring privacy and user control. We will explain techniques that make SLMs more efficient, such as Low-Rank Adaptation (LoRA) and model quantization. We will discover new hardware trends that allow AI models to run on personal devices on CPU and Neural Processing Units (NPUs).
The practical benefits of SLMs will be explained, including enhanced privacy, independence from large tech companies, and greater control over AI applications. You will learn how to host your own language models at home or in your company, using tools like Ollama and LM Studio. We’ll also discuss innovative ways to integrate LLMs and SLMs into your daily workflow, including the latest prompt engineering techniques, and ponder whether prompt engineering will remain relevant as models improve.