Friday, February 7, 2025

Small Language Models Examples, Benefits And Use Case

In this article we will discussing SLMs Definition, What are SLMS, Small language models examples, Benefits, and use cases.

What is a small language model?

A lightweight generative AI model is called a small language model (SLMs). In this sense, the term “small” describes the size of the neural network of the model, the quantity of data it is trained on, and the number of parameters it utilizes to reach a conclusion.

Compared to large language models (LLMs) and SLMs demand less memory and processing resources. They are therefore appropriate for both on-premises and on-device implementations.

What are Small Language Models?

Smaller copies of its LLM counterparts are basically what SLMs are. Compared to LLMs that contain hundreds of billions or even trillions of parameters, they usually have a few million to a few billion. Several benefits result from this size difference:

  • Efficiency: SLMs may be implemented on smaller devices or even in edge computing settings since they need less memory and processing power. Real-world applications such as personalized mobile assistants and on-device chatbots are made possible by this.
  • Accessibility: A wider range of developers and organizations may use SLMs since they demand fewer resources. This democratizes AI by enabling individual researchers and smaller teams to investigate the potential of language models without having to make large infrastructure expenditures.
  • Customization: SLMs may be more easily adjusted for certain activities and domains. This makes it possible to develop customized models for specialized applications, which improves accuracy and performance.

How does Small Language Models Work?

SLMs are trained on large text and code datasets, just like LLMs. Nonetheless, a number of methods are used to reduce their size and increase their effectiveness:

  • Knowledge distillation: It is the process of moving information from an LLM that has already been trained to a smaller model that captures its essential features without all of its complexity.
  • Pruning and quantization: This are two methods that further minimize the model’s size and resource needs by eliminating superfluous portions and decreasing the accuracy of its weights, respectively.
  • Efficient designs: With an emphasis on performance and efficiency optimization, researchers are constantly creating new designs especially made for SLMs.

Advantages and Drawbacks

One benefit of small Language Models (SLMs) is that they may be trained on relatively little datasets. Their small size makes deployment on mobile devices easier, and their streamlined structures improve interpretability.

SLMs’ capacity to process data locally is a noteworthy advantage, which makes them especially useful for Internet of Things (IoT) edge devices and businesses subject to strict privacy and security laws.

Nevertheless, there is a trade-off when implementing small language models. SLMs have more limited knowledge bases than their Large Language Model (LLM) counterparts since they are trained on fewer datasets. Furthermore, compared to bigger models, their comprehension of language and context is typically more limited, which might lead to less precise and nuanced replies.

Small language models examples

The following are Small language models examples including:

  • DistilBERT: It is a lighter, faster, and smaller version of the breakthrough natural language processing (NLP) model BERT.
  • Orca 2: Microsoft created Orca 2 by optimizing Meta’s Llama 2 using superior synthetic data. With this strategy, Microsoft was able to match or even outperform bigger models, especially when it came to zero-shot reasoning tasks.
  • Phi 2: Designed to be effective and adaptable in both cloud and edge deployments, Microsoft’s Phi 2 is a transformer-based SLM. Microsoft claims that Phi 2 has cutting-edge performance in logical thinking, common sense, language comprehension, and mathematical reasoning.
  • BERT Mini, Small, Medium, and Tiny: Google’s BERT concept has been scaled down to accommodate various resource restrictions, resulting in BERT Mini, Small, Medium, and Tiny. From the Mini, which has just 4.4 million characteristics, to the Medium, which has 41 million, they provide a variety of sizes.
  • MobileBERT: MobileBERT is specifically made for mobile devices, as the name implies.
  • T5-Small: There are many sizes available for Google’s Text-to-Text Transfer Transformer (T5) device. Performance and resource utilization are intended to be balanced in T5-Small.

Small language models use cases

Businesses may modify SLMs to suit their unique requirements by fine-tuning them using domain-specific datasets. Because of their versatility, tiny language models may be used in a wide range of practical applications.

Chatbots

SLMs can fuel customer care chatbots, which can quickly and in real-time react to enquiries because to their low latency and conversational AI capabilities. They can also act as the foundation for agentic AI chatbots, which can do more than just respond to queries; they can also carry out tasks for users.

Summarizing content

Llama 3.2 1B and 3B models, for instance, may be used to generate action items like calendar events and summaries conversations on a smartphone. Gemini Nano is also capable of summarizing conversation transcripts and audio recordings.

Generative AI

Text and software code may be completed and generated using compact models. For example, code may be generated, explained, and translated from a natural language prompt using the granite-3b-code-instruct and granite-8b-code-instruct models.

Language translation

A lot of tiny language models can translate between languages fast since they are bilingual and have been taught in languages other than English. They may provide translations that are almost exact while preserving the subtleties and meaning of the source material because of their capacity to comprehend context.

Predictive maintenance

Lean models are tiny enough to be immediately deployed on local edge devices, such as sensors or Internet of Things (IoT) devices, for predictive maintenance. In order to anticipate maintenance requirements, manufacturers can use SLMs as instruments that collect data from sensors mounted in machinery and equipment and analyze that data in real-time.

Sentiment analysis

SLMs are adept in objectively sifting and categorizing vast amounts of material in addition to processing and comprehending language. As a result, they may be used to analyze text and determine its sentiment, which helps in comprehending client feedback.

Vehicle navigation assistance

The onboard computers of a car may run a model as quick and small as an SLM. Due to their multimodal capabilities, tiny language models may, for instance, identify impediments surrounding a vehicle by combining speech instructions with picture categorization. In order to assist drivers in making safer and better-informed driving decisions, they can even utilize their RAG capabilities to get information from road laws or highway codes.

Drakshi
Drakshi
Since June 2023, Drakshi has been writing articles of Artificial Intelligence for govindhtech. She was a postgraduate in business administration. She was an enthusiast of Artificial Intelligence.
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