What are Small Language Models (SLMs) – A Brief Guide | USAII®

1. ARE THEY BETTER ALTERNATIVES TO THEIR LARGER COUNTERPARTS? © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved. WWW.USAII.ORG

2. It requires no mention of how the Natural Language Processing (NLP) technology transformed the entire way we used to live and work. It powers tools like Siri and Alexa, makes our communication with computer systems natural, and makes communication with advanced Generative AI tools easier. Large language models revolutionized natural language processing. LLMs are highly powerful AI models trained on huge amounts of datasets, usually in millions of terabytes, and they can efficiently generate human-like text, translate languages, and answer user queries. The Problem This sheer size and computational power raise concerns about their accessibility and sustainability. It has led to demand for more efficient, compact, and resource-friendly alternatives. The Solution Small Language Models (SLMs) Small language models (SLMs) have been gaining huge popularity recently. This whitepaper will help you understand everything about SLMs in detail. © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved. WWW.USAII.ORG

3. What Are Small Language Models? Small language models, as the name suggests, are language models with a significantly smaller number of parameters as compared to large language models. While LLMs can have billions and even trillions of parameters, SLMs have only a few million or a few billion parameters. This reduced size, of course, comes with trade-offs but also offers huge advantages to organizations and professionals. They are designed to perform specific tasks efficiently and mostly focus on a narrower domain or a particular set of functionalities. We can consider that SLMs are not intended for general-purpose tasks like LLMs. They are specialized tools with a high degree of optimization for particular applications and this specialization helps the SLMs to achieve equal and even higher performance than LLMs with a very nominal computational resource. Key Features and Benefits of Small Language Models SMLs offer a lot of benefits and are suitable for a wide range of applications where LLMs can struggle. Here are some of the features and benefits of SMLs explained: Accessibility Efficiency Small organizations or individuals who do not have sufficient resources can also implement SLMs without high-end infrastructure. They also do not rely on cloud- based infrastructure and therefore can be deployed on premises requiring high data privacy and security. Since SLMS don't need huge computational power like LLMs, they can be used on small devices that operate with limited resources like smartphones or IoT devices. Customization SLMs can be easily fine-tuned and customized for niche-specific tasks and domains. Faster Inference SLMs have very few parameters to process which makes them faster to provide response to user inputs. This feature is beneficial in real-time applications where fast decisions are essential. Privacy As the SLMs can run on local devices, they are great at ensuring user privacy by avoiding the need to send data to external servers. © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved. WWW.USAII.ORG

4. How Small Language Models Work? Small language models also employ neural network-based architecture like LLMs called the transformer model. These transformers are the fundamental components of NLP and serve as building blocks of models. Moreover, SLMs use three important techniques to become faster, smaller, and more efficient: DISTILLATION PRUNING QUANTIZATION PRUNING This technique involves training SLMs through knowledge transfer from the larger model (teacher) to a smaller model (student). In this technique, the goal is to transfer what the teacher model has learned to the student model by compressing the information without losing its performance. This process helps retain equal accuracy as well as make the model more manageable Methods of knowledge distillation: Response-Based Feature-Based Relation-Based The student model learns to replicate the final output layer of the teacher model, often using "soft targets" for more nuanced information. Focuses on replicating the intermediate layers of the teacher model, helping the student extract similar patterns from data. Trains the student to understand relationships between different parts of the teacher model, emulating complex reasoning processes. PRUNING This technique, as the name suggests, involves pruning out information not needed or important such as neurons or parameters that are not involved much in the overall performance. Professionals use pruning techniques mostly to shrink the model without impacting its accuracy. But it can be a complex task, and aggressiveness should be avoided in cutting out too much that could later impact the model's performance. Pruning is a great way to reduce size and maintain original-like performance. So, it is widely used for creating SLMs QUANTIZATION Quantization refers to the use of fewer bits for storing the model's numbers. Mostly, the models use 32-bit numbers whereas in this method, it is compressed to 8-bit which helps minimize storage and processing requirements and also doesn't affect the accuracy. Thus, it becomes an important technique for models that need to be deployed in resource-constrained devices like smartphones. Quantization technique is best suited for deploying SLMs on devices with limited resources. © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved. WWW.USAII.ORG

5. Examples Of Small Language Models There are several small language models developed with each having its own strengths and specialization. Here are some of the most popular examples of SLMs: Llama is Meta's LLM and its 3.2 version comes in 1 and 3 billion parameter sizes. It is a small, fast, cheap, and lighter version of BERT that retains much of its language understanding capabilities. distilbert It has been designed with the same technology as Google's Gemini and is offered in 2,7, and 9-billion parameter sizes. Users can avail of it through Google AI Studio or Kaggle platforms. It has been designed for mobile devices and optimized for higher speed with minimum size without compromising performance MobileBERT GPT-4o mini belongs to OpenAI's GPT-4 family of AI models. It is smaller and a cost-effective version of GPT-4o. Most importantly, it is a multimodal AI meaning it can generate text outputs from both text as well as image prompts. TinyBERT further shrinks the size of BERT by employing techniques like knowledge distillation as we discussed above and maintains performance TinyBERT It is a lightweight version of ALBERT that focuses on parameter efficiency. ALBERT-lite These are powerful, yet resource-friendly models used for a variety of applications (as discussed in later sections). Comparing LLMs and SLMs FEATURE LLMs SLMs SIZE Billions/trillion of parameters Millions/billions of parameters PERFORMANCE Generally higher on complex tasks Good performance on specific tasks COMPUTATIONAL COST Very high Lower Can be deployed on resource-constrained devices DEPLOYMENT Requires substantial resources FINE-TUNING Resource-intensive Less resource-intensive GENERALIZATION Stronger generalization abilities More specialized, less generalizable © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved. WWW.USAII.ORG

6. Small Language Model Use Cases & Applications Small language models can be fine-tuned according to organizations' specific needs and on domain-specific datasets. And therefore, they are used for a wide range of applications such as: Chatbots With their low latency and conversational AI capabilities, SLMs are highly suitable for customer service chatbots and respond to user queries in real time. Content Summarization The SMLs can be used to summarize discussions on smartphones or create calendar events and similar action items. Generative AI Smaller models like granite-3b-code-instruct and granite-8b-code -instruct models can be used to complete or generate texts and codes. Language Translation Many of the models are trained in various languages and not just in English. Therefore, they can be used to understand the context of text input and translate them into the desired language without distorting their original meaning. Predictive Maintenance This may sound too much, but some models can be smaller and deployed directly on edge devices including IoT devices and sensors to gather data from the source directly and analyze them in real-time to predict maintenance needs. Sentiment Analysis On top of understanding and processing languages, these compact models are also useful in objectively categorizing huge amounts of text. That means they can analyze text and understand the sentiment behind it. It is useful in cases like understanding customer feedback. Vehicle Navigation Assistance SLMs are fast and compact that can be integrated into a vehicle's computer system. This will assist in features like voice commands, image classification, and identifying obstacles around the vehicles. From mobile applications to embedded systems and personalized assistants to edge computing, these applications of small language models can prove to be revolutionary in the years to come. © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved. WWW.USAII.ORG

7. Limitations Of Small Language Models Just like all other technologies, even though SLMs come with huge benefits and applications, they are not bereft of certain limitations and challenges. This includes: Hallucination The small language models can also hallucinate and therefore it becomes essential for users to validate their output from genuine sources. Performance They struggle to perform like LLMs for complex tasks that require a broader understanding of language as they are fine-tuned for specific tasks. Bias If there is bias in larger models then smaller models can inherit them as well which can impact their outputs. Limited Generalization As most of the SMLs are made for specific tasks, they lack the wider knowledge like their larger equivalents, and thus may not be well suited for general tasks. CONCLUSION Small language models demonstrate huge advancements in NLP technology that offer both performance as well as efficiency. Of course, they are not as capable as their larger counterparts, but their smaller size and lower computational requirements make them the preferred choice for a wider environment and applications including mobile and edge devices. With the ongoing research on this technology, we can expect it to become even more powerful and versatile. Moreover, their easy accessibility and on-device processing power will introduce greater democratization of AI and enhance the privacy and security of user data. SLMs have a bright future ahead. But what about you? Are you prepared enough to leverage the power of SMLs in your business applications? If not, then learn how to develop, deploy, and use SMLs across various industries with our top AI certifications. © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved. WWW.USAII.ORG

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