these models require immense efforts and resources in order to maintain their advanced features and technology.
But, the small language models, known as SLMs, represent a more compressed alternative, which offers multiple advanced artificial intelligence capabilities requiring reduced computing demands. Compared with large language models, SLMs are more efficient and don’t need huge servers in order to operate, being built for real-time performance to run on smartwatches, smartphones, or even tablets.
As the name suggests, SLMs represent a smaller and more compressed version of large language models and feature fewer parameters, usually between a few million to a few billion. On the other hand, LLMs require a significantly higher number of parameters, billions or even trillions in order to operate and offer valuable information for users.
Small language models need less energy consumption and also fewer hardware requirements, which makes them perfect to be implemented in startups or for academic researchers. So, because SLMs don’t need as many resources, they become more accessible for various developers or companies, which democratizes AI. This way, smaller teams have the same chance to discover how powerful language models are without having to make significant investments.
Also, SLMs become more flexible to allow customization for specific tasks or certain domains of activity. So, small language models can be easily tailored for different niches, which leads to an increased performance in tasks.
- Phi-3.5 developed by Microsoft
The Phi-3.5 small language model has become popular due to its advanced capabilities available at a significantly lower cost. Developed by Microsoft, this SLM provides accessible artificial intelligence features and is able to handle long documents or complex tasks with multiple conversations. With 3.8 billion parameters and 128K tokens of context length, Phi-3.5 by Microsoft is a considerable rival for other SLMs such as Llama 3 or Gemma 2.
- Llama 3, developed by Meta
This small language model provides excellent efficiency and power using 8 billion parameters, making it a great option for various tasks such as sentiment analysis or answering questions. Regarding its smaller size, Llama 3 by Meta is an open-source alternative that allows developers to customize it in order to adapt to different contexts and offers impressive speed without neglecting the accuracy of answers.
- Mixtral 8x7B developed by Mistal AI
This small language model truly represents an impressive model in the artificial intelligence industry because it focuses at the same time on open accessibility and efficiency. Popular based on its open technology, Mixtral 8x7B has 7 billion parameters and can process large contexts up to 32k tokens. This SLM also supports multiple language requests such as French, Italian, German, Spanish, and, of course, English.
- Gemma 2 was developed by Google
With 2 billion parameters, Gemma 2, developed by Google, delivers impressive performances for text generation and translations. Compared with other, much more powerful language models such as OpenAI o1, this small language model is not really suitable for complex reasoning, but more for real-time requests. Even more so, Gemma 2 can be customized for different specific tasks in order to provide more accurate answers.
- OpenELM, developed by Apple
OpenELM represents a very adaptable model that has a parameter range from 270 million up to 3 billion, and it’s specially designed for those situations where companies need low-latency responses. So, OpenELM is the perfect choice for real-time tasks on smaller devices.
We must admit that the technology and artificial intelligence evolution has reached a high point because more and more companies are looking to develop various large and small language models in order to provide the best performance. Keep in mind that smaller doesn’t necessarily mean that a model provides lower performance. In some cases, such as real-time tasks for companies, a smaller model can be the perfect solution for providing faster and more accurate responses.
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