Language models play an essential role in the field of artificial intelligence. They use human language patterns to understand and respond to text. This allows them to assist in duties such as translation and communication.
While large language models (LLMs) have been widely used in recent years – they have limitations. They have high computing demands, operating costs, and increased privacy concerns. This is where SLM have an advantage. They offer speedier, more efficient solutions that are ideal for edge devices and real-time applications.
The rise of small language models as alternatives to large language models
Small language models (SLM) are becoming popular as efficient and accessible alternatives to large language models. They are designed to be smaller and more resource-efficient from the outset. It is for this reason that they can easily be deployed in a resource-constrained environment. Their smaller model size translates to faster processing speeds, enabling real-time applications and quicker responses. Talking of the market traction of SLM, the market is projected to reach $20.71 billion by 2030.
Purpose of the guide
If you are into building smart apps, working with edge devices, or exploring real-time AI, this guide is for you. The post breaks down what SLM are, the various SLM applications, how SLM work, the benefits they offer, and various other such aspects.
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What are Small Language Models (SLM)?
1. Definition of SLM and how they differ from LLMs in size and scope
Small language models (SLM) are artificial intelligence models that can process, understand, and generate natural language content. In comparison to large language models, they have a smaller number of parameters and a simpler architecture. As such, they are more efficient, require less computational memory and power, and are easier to train.
2. Typical parameter range
If we compare LLM vs SLM (Small Language models), the latter typically range from millions to a few billion parameters (1 million to 10 billion parameters). LLMs, on the other hand, can have hundreds of billions or even trillions of parameters. The small language models, although significantly smaller, can retain core NLP capabilities, including text generation, summarization, question answering, and translation.
3. Key use cases where SLM excels
Companies can fine-tune SLM on domain-specific datasets to customise them to meet business needs. Let’s have a look at some SLM use case and see how SLM are used across industries –
a. Chatbots
Small language models have conversational AI capabilities and low latency. They can power customer service chatbots, responding rapidly to queries in real-time. They can equip agentic AI chatbots with the ability to act and think independently, enabling them to handle simple tasks locally while saving resources and time.
b. Content summarization
Models, such as Llama 3.2 1B and 3B, can be used to summarize discussions. It can summarize news articles, internal reports, or product reviews to help businesses take quick action. Models like Gemini Nano can help summarize transcripts of conversations and audio recordings.
c. Language translation
Travelers can use small language models to provide on-device translation. Researchers have trained many small language models in languages other than English. As such, they can translate between languages quickly while retaining the nuances and meaning of the text.
d. Sentiment analysis
Small language models (SLM) can help businesses determine whether a customer’s feedback is positive or negative and take actions based on the result to enhance the customer experience. They are skilled at classifying and objectively sorting huge volumes of text.
e. Predictive maintenance
SLM can support predictive maintenance by providing quick and intelligent insights from machine data. They can analyze logs, explain alerts, suggest next steps, and reduce downtime. Since they run on small devices, they can be used remotely.
How Does SLM Work?
Similar to large language models, small language models employ a neural network-based architecture, also known as the transformer models. Let’s understand how Small language models work –
1. Core technology: transformer architecture and self-attention mechanisms
Small language models (SLM) use transformer architecture. Transformers can be inferred as the brain behind the language models. By using “self-attention”, they figure out which words in a sentence are most relevant to each other. By doing this, the model understands the context.
For instance, in the sentence “Although John dropped the glass, it didn’t break.”, the models focuses on the nearby words dropped and glass and infers that the word “it” is most likely talking about the glass.
2. Techniques for making models “small”
i. Distillation
The technique involves transferring knowledge from a “teacher” model to a smaller “student” model. The goal here is to refine the learnings of the teacher models, compress them, and transfer them into the student models. All this, without significantly compromising its performance.
This also ensures that maximum knowledge is transferred to the smaller models while reducing its size and computational needs. This technique also helps the student models to obtain the prediction-making ability and the nuances of the teacher models.
ii. Pruning
Here, parts of the models that aren’t important, such as neurons and parameters that don’t contribute much, are removed. This technique helps reduce the model’s size while retaining accuracy. With pruning, the risk is that you might cut too much, impacting the models’s overall performance.
iii. Quantization
The technique entails using fewer bits to store the models’ numbers. For instance, instead of 32-bit numbers, 8-bit values are used. This way, the models become smaller in size, run faster, and have minimal impact on the overall accuracy of the models.
3. Training on smaller or domain-specific datasets
Researchers train small language models (SLMs) on curated and selective datasets tailored for particular tasks or industries. For example, developers train an SLM for healthcare on medical data.
4. Examples
a. Next-word prediction
SLM are trained to predict the next word in a sequence based on the preceding context. To do that, the model goes through the input sequence, identifies linguistic and statistical patterns, and assigns probabilities to different words in its vocabulary to find out what the next word might be.
b. Context understanding
This is where the models grasps the meaning and relationships within the input sequence to make informed predictions. Furthermore, the context can include grammatical relationships between words, semantic meanings, and the overall topic.
SLM vs. LLMs—Key Differences
Comparison table
| Aspect | SLM | LLMs |
| Size | Millions to billions | Billions to trillions of parameters. |
| Computational needs | More compact and efficient architecture | Complex and resource-intensive |
| Training data | Trained on a smaller and more targeted dataset, particular to a task or domain. | Trained on a massive and diverse dataset. |
| Versatility | Limited versatility; performs well on specific or narrow tasks for which it was trained. | High versatility; can handle a wide range of tasks, topics, and domains with strong performance. |
| Cost | More cost-effective due to its smaller size and lower resource demands. | Due to resource and size requirements, LLMs can be expensive to use, especially for large-scale applications. |
| Speed | Faster response time due to smaller size | Slower response time due to high computational load |
Benefits of Small Language Models
Let’s have a look at some of the benefits of small language models (SLM).
1. Lower computational and memory requirements.
Small language models are more compact and efficient than large language models – as they require less memory and lower computational power. This makes them ideal for resource-constrained environments such as mobile apps and edge devices. Because of their lightweight nature, these models function effectively even when answering user queries offline without a data network.
2. Faster inference and real-time application suitability
Since small language models require fewer parameters to process, they are ideal for generating responses and analyzing data at a much faster rate. They are also suitable for applications that depend on real time responses due to their efficiency, and can perform tasks such as live translation, chatbots, and on-device processing.
3. Easier deployment on edge devices, mobile, and IoT
SLM are easier to deploy on edge devices, mobile devices, and IoT devices because they require less storage and have a smaller size. Another reason that they are ideal for these devices is that they require low processing power and low energy consumption. This ability of SLM also helps extend battery life apart from supporting fast response time and on-device actions.
4. Enhanced privacy and on-premises processing
Thanks to their smaller size, SLM can be deployed in on-premise setups and private cloud environments. This enables better mitigation of cybersecurity risks, as well as enhanced data protection, particularly in sectors such as healthcare and finance, where both security and privacy are of importance.
5. Easier customization and fine-tuning for niche tasks
Small language models (SLM) can easily be fine-tuned for domain-specific tasks. Again, owing to their smaller size, they can quickly adapt to specialized and niche-based domains such as education, customer support, healthcare, and legal document analysis, among others.
Limitations and Challenges
1. Reduced versatility and generalization compared to LLMs
Like we mentioned in the post, SLM comes with limited parameters. This limits their ability to adapt to new situations and handle a wide range of topics. In contrast to larger models, they may struggle to transition smoothly between different tasks or comprehend unfamiliar inputs. SLM also tend to have reduced generalization compared to LLMs, as they are trained on a smaller, narrower dataset. They may not perform well when tasks beyond their training dataset are given.
2. Potential for lower accuracy on broad or open-ended tasks
In case complex or loosely defined questions are presented – small language models may not be able to deliver accurate answers. Neither can they perform with accuracy in open-ended tasks that have no single absolutely correct answer. That’s because such tasks require context, reasoning, and diverse knowledge.
3. Importance of high-quality, curated data for best results
Small language models are often dependent on tailored datasets for specific tasks that can be scarce or hard to create. Also, the ongoing efforts to update and curate the training data can be a continuous challenge. Next, it can be further challenging to ensure that the training data is unbiased.
How to Build or Use an SLM?
Here are the steps to build or use small language models (SLM) –
1. Using pre-trained SLM from platforms like Hugging Face
To utilize pre-trained small language models (SLM) from platforms like Hugging Face, you can leverage the Hugging Face Transformers library. The library provides an interface for loading, and fine-tuning pre-trained models for various tasks.
2. Steps for training or fine-tuning your own SLM
Training a small language model involves collecting and cleaning relevant data, then splitting it into training, validation, and test sets. Once that’s done, the team chooses a pre-trained SLM suited to the task and fine-tunes it using a well-defined training loop with proper hyperparameters. Lastly, they evaluate the models on unseen data and refine them based on results.
3. Popular SLM and Tools
i. Llama 3.1 8B
It is a powerful, multilingual, large language models optimized for dialogue use cases. It has 8 billion parameters, it is trained on 15 trillion tokens, and can handle a wide range of tasks, ranging from conversation to text generation.
ii. Gemma2
Gemma 2 is a family of open language models, with variants ranging from 2 billion to 27 billion parameters. These models are developed by Google and are suitable for applications that can work with less processing power.
iii. Qwen 2
Qwen2 is a family of large language models developed by Alibaba’s DAMO Academy. They are designed for high-performance language understanding and generation tasks. They consist of 0.5 billion to 72 billion parameters.
iv. Mistral Nemo
Mistral NeMo is a 12 billion parameter, open-source language models developed through a collaboration between Mistral AI and NVIDIA. It is designed as a “small” language models, meaning it is more compact and efficient than larger models, making it suitable for various applications requiring lower computational resources.
v. Phi-3.5
Phi-3.5 is a family of small language models (SLM) developed by Microsoft, designed to offer powerful capabilities similar to larger language models (LLMs) while being more resource-efficient. They excel at tasks that require quick reasoning and solving mathematical and logical problems.
Conclusion
As can be established from the blog, unlike large language models, SLM need fewer resources and less computing power. Although they may lack certain capabilities present in LLMs, they can still prove to be an affordable, scalable, and efficient option for SMEs seeking faster AI solutions that are effective for business operations. Advancements in models architectures and ongoing training techniques hold greater potential to improve SLM and make it more accessible for businesses and industries.
