Small Language Models and the Role They’ll Play in 2025

Even if correctly implemented, AI benefits do not always materialize, and costs can quickly spiral out of control – these two emerging challenges are making it difficult for Chief Information Officers to deliver value with AI, according to Gartner. Across industries, there are many companies struggling with a similar scenario. AI solutions promise a lot but – if not well-suited – do not always deliver. Increasingly, small language models that are more industry specific and easier for generative AI development services to take advantage of are becoming a viable option for businesses of various sizes. This article will explain what small language models are, how they work and what their potential use cases are.

What are small language models (SLMs)?

Small language models (SLMs) are artificial intelligence (AI) systems that process, understand, and generate natural language content. Smaller in scale and capability than large language models (LLMs), the parameters of SLMs typically range from a few million to a few billion, while LLMs can contain hundreds of billions or even trillions of parameters.

What’s the difference between SLMs and LLMs?

Small language models are more compact and efficient compared to larger models, so they need considerably less computational power and memory than larger models. Increased efficiency makes them more accessible to individuals and organizations with limited resources. They are also suitable for scenarios where AI inference needs to be performed offline without access to a data network when a model generates a response to a user’s query.

How do small language models work?

In general, small language models work similarly to large language models, in that they take advantage of a transformer model (neural network-based architecture), acting as a part of a generative pre-trained transformer. Large language models operate as a base for small language models.

The benefits of small language models

There are several advantages to implementing small language models. Crucially, SLMs:

• Generate responses quickly with fewer parameters, making them suitable for real-time applications such as chatbots and voice assistants,
• Can be fine-tuned on specific datasets in a more straightforward way, as tailoring their performance to particular domains or tasks enables the creation of specialized and accurate language models,
• ️Adapt to new data and functions faster than larger models, making them more flexible and versatile. With less data and computational resources required to train them, training costs are significantly reduced,
• Enhance data protection and threat management for companies since improved privacy and security measures can be introduced due to SLMs’ smaller size.
• Refine those models doesn’t require extensive computer power.

The disadvantages of using small language models

Utilizing small language models presents certain challenges. SLMs may struggle with:

• Completing more complex tasks due to smaller model size and less proficiency connected to its lower performance and being fine-tuned for very specific tasks,
• Adapting to new tasks and domains due to stumbling with reduced generalization leads, meaning context switching might be an issue,
• Hallucinations and potential bias in their responses, just as with LLMs.

What are some examples of small language models?

There are various small language models fine-tuned for specific industry needs on the market. Here are five of the most notable ones, listed in alphabetical order:

Gemma 2

Gemma 2 is a general-purpose language model designed for a variety of tasks. It aims to be lightweight, while still delivering high performance across different applications. Although specific details about its architecture and capabilities are not well-documented compared to other models, Gemma 2 focuses on efficiency and adaptability in natural language processing tasks.

MiniCPM-Llama3 v2.5

MiniCPM-Llama3 v2.5 is a streamlined version of the Llama3 model, designed for efficiency without compromising performance. It is particularly well-suited for tasks that demand quick response times and effective processing of smaller datasets. Although specific metrics and comparisons may be limited, it is recognized for its effective balance between size and capability.

Mistral Small

Mistral Small is an efficient language model developed by the French start-up Mistral AI. It excels in various language-based tasks, including text generation, translation, and summarization. This multilingual model is cost-effective and prioritizes safety. Mistral Small is accessible through APIs and cloud platforms, making it a valuable tool for businesses and developers. It’s an excellent choice for diverse language-related applications.

OpenELM

OpenELM is an open-source language model that prioritizes transparency and accessibility, allowing users to modify and adapt the model for specific applications, making it a popular choice among researchers and developers seeking customizable solutions in natural language processing.

Phi-3

Phi-3 is a compact language model designed to enhance reasoning capabilities while performing standard language processing tasks. It employs advanced techniques to improve logical reasoning and comprehension, making it well-suited for applications that require a deeper understanding of context and intent.

General small language model use cases

Using domain-specific datasets, businesses and organizations can fine-tune SLMs to meet their specific requirements. Small language models are adaptable to a wide range of real-world applications. There are four general SLMs use cases:

• Chatbots, tailored to businesses specific needs, that can respond rapidly to real-time queries and function as embedded solutions in, for example, customer service or as internal chatbots that support employees’ inquiries,
• Real-time translation of speech or text, creating content for various purposes, and translating entire documents. Working as AI agents that enable fast and reliable document summarization and analysis.
• Extracting information from unstructured text, identifying and correcting errors in data, and improving data quality for analysis.
• Operating voice-controlled consumer devices (smart speakers, mobile phones, smart home platforms) and all sorts of IoT devices used in manufacturing and industrial solutions.

Small language models (Microsoft use cases)

Microsoft has been focused on domain-based LLMs. With cooperation with their partners, they launched new models and announced several new partner solutions in its AI model catalog, highlighting advancements across various industries:

• Bayer: Introduced E.L.Y. Crop Protection, a specialized AI model that enhances sustainable crop protection and compliance. Scalable and customizable, it supports regional and crop-specific requirements for agricultural entities.
• Cerence: Launched CaLLM Edge, an automotive-embedded AI model for in-car controls and offline functionality that delivers cloud-like responsiveness – even in limited connectivity scenarios.
• Rockwell Automation: Released FT Optix Food & Beverage, an industrial AI model that offers asset troubleshooting and manufacturing process insights to frontline workers in the food and beverage industry.
• Siemens Digital Industries Software: Introduced a copilot for NX X software, an AI-powered tool that helps CAD designers with natural language queries, technical insights, and design optimization recommendations.
• Sight Machine: Developed Factory Namespace Manager, a model that standardizes factory data naming conventions for AI readiness – enabling improved production, energy efficiency, and supply chain integration.

Small language models (SLMs) in 2025 and beyond

The future of the LLM market is leaning towards specialized, fine-tuned models that deliver enhanced performance, reduced costs, and more precise domain expertise. As businesses increasingly understand the competitive advantage offered by customized AI solutions, industry-specific fine-tuning using SLMs will become a crucial aspect of AI strategy.

If you are interested in implementing small language models in your organization, contact us by using this form. One of our AI experts will assist you in choosing a suitable fine-tuned model.