Adoption of Smaller Language Models Growing in Enterprises

Small Language Models (SLMs) Revolutionizing AI for Enterprises

Key Takeaways:

• SLMs gaining traction over large language models (LLMs) for enterprises
• SLMs offer more control, data security, and cost-efficiency
• Top SLMs include Llama-2-13b, CodeLlama-7b, Mistral-7b, Mixtral 8x7b, Phi-2, and Orca-2

Summary:

Small language models (SLMs) are transforming AI for enterprises by offering more control, data security, and cost-efficiency compared to large language models (LLMs). These smaller SLMs are ideal for resource-constrained environments, faster training, and inference times. However, there are challenges in adopting SLMs, such as unexpected changes to platforms and the need for specialized expertise. Companies are looking for ways to address these challenges, with startups like OctoAI and Databricks offering solutions.

Swapping Different SLMs

Another issue is the need to build systems that allow for easily swapping different SLMs, as well as the challenge of integrating SLMs with legacy systems. Enterprises need to carefully measure the quality tradeoffs between SLMs and LLMs to ensure they align with their tasks.

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