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Meet SLM models – the small revolution in language AI

1 April 2024

Anat Bielsky

What is a Small Language Model?

A Small Language Model (SLM) is a machine learning model proficient in handling natural language processing tasks. The term "small" refers to the neural network's size, parameter count, and training data volume. While previous models were considered small with hundreds of millions of parameters, today's numbers have escalated. For example, Microsoft's SLM model, Phi-2, is trained on 2.7 billion parameters, while Meta's SLM model, LLaMa, boasts 7 billion parameters. This is in contrast to GPT4's LLM model, trained on 1.8 trillion parameters, and Google's LLM model, Gemini Ultra, with 1.5 trillion parameters.

Until recently, the substantial resources required for LLM models presented barriers, favoring large tech companies in the AI market. The emergence of SLM models has begun to break down these barriers, empowering smaller businesses to develop and utilize their own language models.

Advantages of SLM models:

  • Lighter and faster compared to LLM models.

  • Can specialize in specific areas or tasks, e.g., medicine or law.

  • Require fewer computer resources and less memory, making them suitable for resource-limited applications.

  • More conducive to real-time execution thanks to their small size.

  • Train faster due to requiring less data.

  • Easily implementable on mobile devices.

  • Easier to maintain and update, thanks to simplified data structures.

  • Simple integration into software and websites without extensive infrastructure changes.

  • Compatible with on-premises systems, not restricted to cloud usage due to their small size.

Despite their small size, SLM models can achieve similar performance to LLM models, while reducing operating costs and ensuring a secure and managed environment. They are gaining popularity in various applications, particularly in sustainability and data conservation contexts. They can be applied to various tasks, including text creation, summarization, machine translation, question answering, and emotion analysis.

Here are some examples of SLM models developed by different companies:

  • Microsoft's Phi 2 model is an SLM model based on transformer technology. It is designed to be efficient and versatile, capable of operating both in the cloud and on network end devices. According to Microsoft, Phi 2 excels in areas such as mathematical thinking, language comprehension, and logical reasoning.

  • Google has developed smaller versions of the Gemini model to adapt it to devices with varying resource limitations. These versions come in a range of sizes, from the smallest mini, with only 4.4 million parameters, to the medium, which contains 41 million parameters.

SLM Model Limitations:

Despite the numerous advantages of SLM models, there are also limitations to consider. The primary constraint arises from their fewer parameters, resulting in a more restricted knowledge base and limited ability to process and generate text compared to LLM models. Consequently, SLM models may prove less efficient for tasks requiring a comprehensive understanding of language and context. Furthermore, transferring knowledge from large models to SLMs can be challenging.

Despite these limitations, SLM models offer a promising and effective approach to artificial intelligence, providing specialized field knowledge and cost-effective solutions for various applications. As technology evolves, these models are expected to play an increasingly significant role in our lives.


What is a Small Language Model (SLM)? Definition & Examples (

EECS-2023-141.pdf (

Small but Powerful: A Deep Dive into Small Language Models (SLMs) | by Rosemary J Thomas, PhD | Version 1 | Medium

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