The Big Impact of Small AI: A Guide to Small Language Models

You must have heard of LLMs like ChatGPT, Bing Chat, and Bard. Business, academics, and the consumer sector are very interested and are using these generative AI models. However, their large size and need for resources have limited their accessibility and usability.

On the other hand, AI-driven solutions like small language models are here to rescue businesses with limited computational resource requirements; their small model size, ease of use, and faster speed than large language models (LLMs) resolve the problem of making artificial intelligence more efficient and convenient.

This blog will help businesses to understand everything about small language models, from how they work to the key challenges, architecture, and benefits.

  Generate  Key Takeaways Generating...

• SLMs are perfect for companies with limited hardware capabilities because they demand less processing power than large language models (LLMs).

• Methods such as quantization, pruning, and distillation aid in the accuracy-preserving reduction of huge models. These techniques lessen computing complexity while preserving key SLM functionality.

• Smaller, domain-specific datasets can be used to train SLMs, providing customized solutions with a lesser chance of producing unreliable findings. 

• SLMs are becoming more widely available to organizations thanks to better training techniques and an emphasis on usability and transparency.

What Is a Small Language Model?

Small language models are simple and effective neural networks designed for language problems. Small language models are made to understand and produce human language and fine-tune specific tasks. They are called small language models because they have fewer parameters compared to LLMs such as BERT and ChatGPT.

Small language models offer effective and lighter solutions for programs requiring limited memory or computational power. Small language models (SLMs) are ideal because they can be used in various applications and perform well even on less complex hardware. They are suitable if you do not require all of the advanced capabilities of a large language model.

SLMs can also be customized to perform precisely what you need, which makes them excellent for domain-specific tasks. If your company begins experimenting with GenAI, SLMs are simple and quick to set up.

Small vs. Large Language Models: Which Fits Your Needs?

Are small language models better than large language models (LLMs)? In which scenario can small language models work more accurately than LLMs? These questions often arise when you want to leverage the small language model for your business.

The answer to these questions will depend entirely upon the business requirements!

Here are the key differences between small and large models that can help you choose the right model according to specific requirements.

1. Customization

LLMs are trained on large datasets. These models help great enterprises with complex and specific tasks like customer service, generating natural language, real-time, and human-like responses. LLMs are fine-tuned to specialized data and lack the customization for particular business needs.

On the other hand, small language models are trained on smaller and more focused training data sets, making them personalized for a company or business. This approach can lower the risk of inaccurate results.

2. Compatibility With Devices

One of the main differentiation points of LLM vs. SLM is the ability to run on devices. LLMs need a large data center to be used at their best. However, small language models can also work efficiently on laptops and smartphones.

3. Data Security

Another feature that sets SLMs apart from open-source LLMs is security. Businesses may have to deal with LLM data privacy issues, like the possibility of sensitive data being exposed via APIs.

SLMs, on the other hand, are less likely to leak data because they are not open source and do not share data with any external server.

4. Required Processing Power

Large models like GPT-3 require a lot of processing power, often from several powerful GPUs, and might take months to train. On the other hand, small language models can be trained in a few days.

This demonstrates that SLMs may be developed significantly more quickly, which is advantageous for companies constantly experimenting with their models.

Related Read: How to Make Your Product AI-Driven With LLM?

How Does the Small Language Model Work?

Like large language models, SLMs use the exact same basic mechanisms, such as self-attention and transformer structures. Like LLMs, the transformer model is a neural network-based design used in small language models.

As the building blocks of models, such as the generative pre-trained transformer, transformers have become essential in natural language processing. For SMLs, they use different methods to reduce the size with less computational power consumption of the model.

The Transformer Architecture is Summarized as Follows:

1. The semantics and location of tokens within an input sequence are captured by encoders, which convert the input patterns into numerical representations known as embedded.
2. Transformers "focus their attention" on the most significant tokens because of a self-attention mechanism.
3. The most probable output sequence is produced by decoders using this self-attention process in combination with the encoders' embedded.

Model Compression

Model compression approaches are used to build a smaller model from large and already trained models. Compressing a model means shrinking and fine-tuning language models to a smaller size while maintaining the accuracy at its highest, lowering the risks of generating irrelevant information. Here are some compression model approaches:

1. Distillation

This method involves transferring knowledge from a more extensive complex neural network (instructor model) to a simpler one (student model).
The idea is to take everything the instructor model has learned and transfer learning into the student model without sacrificing too much performance.

This approach allows SLMs to retain much of the accuracy of larger models while being much smaller and less computationally intensive. Using this method, the smaller model picks up on the teacher's final predictions, details, and underlying patterns.

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2. Pruning

Pruning is a model compression technique that includes eliminating the weights or connections from the neural network that are not needed. Pruning focuses on reducing the size of the network without sacrificing accuracy. Different methods are used in pruning, such as eliminating the smallest weights from the neural network.

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Technically, Pruning involves three basic steps as follows:

• First, the original neural network must be trained to a desired level of accuracy.
• Next, the connections or weights to prune must be selected based on specific criteria, such as the weights' magnitude or effect on the network's output.
• Finally, the pruned network must be fine-tuned to restore its accuracy.

3. Quantization method

One model compression method that lowers the accuracy of a neural network's weights and activations is called quantization. It uses fewer bits than the original precision to describe a neural network's weights and activations.

8-bit integers may be used in quantization to represent weights and activations rather than 32-bit floating-point values. This modification decreases both computational complexity and storage requirements.

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Although there will always be some loss of precision, proper quantization approaches can accomplish significant model reduction with very little loss of accuracy.

Examples Of Small Language Models

The well-known companies behind small language models (SLMs) are Microsoft, Mistral, and Meta. These companies have demonstrated that good outcomes can be achieved with limited resources. Let's now examine a few of the most well-known small language model examples.

1. Distilbert

Google's groundbreaking BERT foundation has a lighter version called Distilbert. It is 60% faster and 40% smaller than its previous version. It maintains 97% of BERT's natural language understanding capabilities.

There are other simplified and smaller versions of BERT, such as Tiny, with 4.4 million parameters; Mini, with 11.3 million parameters; Small, 29.1; and Medium, 41.7. One more version, called MobileBERT, is specifically designed for mobile devices.

2. Phi

Microsoft's Phi suite consists of several tiny language models. There are 2.7 billion parameters in Phi-2 and 3.8 billion in Phi-3-mini.

It excels at creating marketing content, running chatbots for customer service, and summarizing intricate documents. It also satisfies Microsoft's high standards of inclusion and privacy.

3. GPT-4o mini

The ChatGPT generative AI chatbot is powered by OpenAI's GPT-4 family of AI models, of which GPT-4o Small is a member. A more affordable, smaller version of GPT-4o is GPT-4o tiny. It is multimodal; it can produce text outputs and accept inputs in images and text.

GPT-3.5 has been replaced by GPT-4o Small, available to ChatGPT Free, Plus, Team, and Enterprise users. Through various application programming interfaces (APIs), developers can access GPT-4o mini.

4. Llama

Meta's line of open-source language models is called Llama. Llama 3.2 has parameter sizes of 1 and 3 billion, even less than the previous 7-billion-parameter Llama 2 version.

Deeper interactions are made possible by Meta's Llama 3, which can comprehend twice as much text as the previous version. It leverages a bigger dataset to improve its capacity to handle challenging jobs, and it is easily integrated throughout Meta's platforms, increasing user access to AI insights.

5. Gemma 2

Gemma 2 is a small language model developed for quick natural language processing tasks. Gemma 2, which is designed for applications that need less processing power, strikes a compromise between speed and accuracy, making it appropriate for use cases like interactive tools, chatbots, and content summaries.

Because of its optimized model architecture and training techniques, it delivers a competitive model performance despite being smaller than larger models.

Advantages of using Small Language Models (SLMs)

We discussed the differences between SLMs and LLMs, but let's also discuss their strengths as small language models and why they're a key GenAI trend for 2025.

• Easily Accessible: Language model exploration and experimentation can be done by researchers, AI developers, and other people without purchasing numerous GPUs (graphics processing units) or other specialist equipment. SLMs are inexpensive and easy to obtain. They are, therefore, a fantastic option for developers and small companies who wish to experiment with AI.
  
  
• More Control Over Security and Privacy: SMLs are small, enabling them to be installed in a private cloud computing environment, improving data security, and managing and mitigating cybersecurity risks. SMLs can be extremely valuable tools for some industries like finance, healthcare, and legal professionals where security and privacy are critical.

 Related Read: Finance and AI: Applications, Benefits, Technologies, & Implementation

• Efficiency in Model's Performance: Little models can perform similarly and even better than their large model counterparts. GPT-4o Small performs better than GPT-3.5 Turbo in terms of language comprehension, question responding, reasoning, mathematical reasoning, and code production LLM benchmarks. The model's performance of the GPT -4 small is similar to that of its larger sibling.

• Lower Costs: Businesses must spend much on development, operational, and infrastructural expenses while working with LLMs. Meanwhile, SLMs do not have the cost of advanced hardware and high-quality data.

• Lower Latency: Small language models (SLMs) have fewer parameters than larger ones, making them respond quickly and reducing the processing time. Let's have an example: the active parameter counts at inference for Granite 3.0 1B-A400M and Granite 3.0 3B-A800M are 400 million for the 1B model and 800 million for the 3B model, respectively, while their total parameter counts are 1 billion and 3 billion. As a result, both SLMs can provide high inference performance with minimal latency.

Challenges of Using SLMs

Like LLMs, there are some risks and limitations associated with SLMs. Businesses should go through these limitations before integrating SLMs into their specific domains.

Less Ability to Perform Complex Tasks

Small language models are trained to perform specific tasks for specific domains. They may lack the ability to perform complex activities that require knowledge of a wide range of topics. This limitation can cause businesses to implement multiple SLMs for different areas, resulting in more complicated AI infrastructure.

Right Model Selection

With specifications in particular areas, many models have entered the market due to the growing interest in SLMs. Selecting the right SML for business needs can be challenging. Without thorough knowledge about the model size, it will be difficult for companies to get the best model.

Biased Responses

It is possible for smaller models to pick up on the bias in their larger counterparts, and this can show up in their results. It is essential to validate SLM results to ensure that the information they generate is accurate.

Need For Accurate Data

Compared to larger models, SLMs are not light in any way, even though their intensity, processing capacity, and scale are smaller. These language models are still being created to handle challenging jobs and requirements.
Accurate training data needs for smaller models remain essential to developing an effective model that produces precise, accurate results. This is why it is necessary to source accurate data from reputable sources.

Small Language Models (SLMs): Latest Development and Emerging Trends

Here are the fascinating trends in AI that will benefit organizations greatly in the future:

Improvement In Training Methods

Researchers are creating new approaches to train the models according to the requirements. These models are more beneficial for businesses that provide practical solutions and for automation, customization, and learning more about the customers.   

Focus on Transparency and Reliability

Understanding how small language models make decisions is essential for building trust in AI systems. The field of study is developing, and with that, the transparency and dependability of SLMs will also increase. This transparency will encourage businesses to integrate SLMs into their processes.

Ease Of Use

As small language models are straightforward, people with no coding skills can apply them to their projects. Businesses may notice increased innovation as employees use AI to perform different complex tasks more efficiently and identify new opportunities.

Future of Small Language Models for Businesses

We have seen that the impact of small language models on artificial intelligence is significant. They are valuable, cost-effective, and appropriate for various business needs without requiring more processing power. These models are perfect for performing tasks like customer service, educational applications, and even instructional applications without consuming any more extensive resources.

In the near future, AI will become more powerful and flexible with advancements in research and development. The gap between large language models (LLMs) and small language models will continue to narrow with better training data, approaches, and model architectures. AI can change every aspect of the business world with new applications and inventions.

Put SLMs into Practice With Signity's Experts

Upgrade your product with the power of small language models by partnering with our team of experts at Signity. With years of expertise and exceptional innovation, our experts have assisted companies worldwide in integrating the best AI-driven solutions into their current systems.

We offer end-to-end assistance to produce valuable outcomes, whether your goal is to improve and optimize processes and customer services or develop cutting-edge AI features. We can ensure that your product is effective and future-ready by guiding you through each stage of the process. Contact us today and get a free quote!