Best Small Language Models (SLMs) - Page 3

JL

Researched and written by Jeffrey Lin

Small language models (SLM) are artificial intelligence (AI) language models that are optimized for efficiency, specialization, and deployment in resource-constrained and compute-limited environments. Similar to large language models (LLMs), SLMs are also engineered to understand, interpret, and generate human-like outputs from a wide array of inputs. Leveraging efficient machine learning (ML) techniques, streamlined architectures, and specialized datasets, these models are often repurposed to perform a select array of tasks to maximize resource efficiency. SLMs can be essential for organizations requiring cost-effective and fast deployment of AI models.

Due to their optimized architectures, SLMs can be deployed on edge devices, mobile platforms, and offline systems, facilitating accessible AI deployment. SLMs differ from LLMs, which focus on comprehensive, general-purpose language models that handle complex, diverse tasks across multiple domains. SLMs are designed to be retrained to maximize specialization and resource efficiency, focusing on targeted applications rather than broad intelligence.

A key difference between SLMs and LLMs is their parameter size, which is a direct indicator of their knowledge base and reasoning potential. SLM parameter sizes typically range from a few million to over 10 billion. Whereas LLMs have parameter sizes ranging from 10 billion to trillions of parameters. In practice, some SLMs are also derived from LLMs through methods like quantization or distillation, which reduce model size for efficiency but do not change the original training data. SLMs differ from AI chatbots, which provide the user-facing platform, rather than the foundational models themselves.

To qualify for inclusion in the Small Language Models (SLM) category, a product must: