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Small Language Models (SLMs): A Rising Generative AI

Updated: Feb 19

Small Language Models Feature Image

Over the past few years, large language models (LLMs) have emerged as impressive generative AI models in natural process language. However, their vast size and resource requirements have limited their applicability and accessibility. Thus, an efficient alternative Small Language Models (SLM) are introduced to modify AI for diverse needs.

However, the cost of training and maintenance of significant models and difficulties in customizing the models come up as challenges for them. Models like Google Bard and ChatGPT require a broad volume of resources such as training data, intricate, deep learning frameworks, storage, and a considerable amount of electricity.

Introduction to Small Language Models

Small Language Models are the smaller versions of their LLM counterparts. Compared to LLMs, they have fewer parameters, ranging from a million to a few billion. SLMs are compact GenAI and are classified by their number of parameters, small network size, and volume of training data. They require less memory and power and processing power than LLMs, making them suitable for on-device deployments. 

SLMs can be a practical choice when there are limitations on resources because they are small in terms of efficiency and architecture. Due to their lightweight structure, SLMs offer a versatile solution for various uses by balancing performance and resource consumption.

Representing few SLMs models via graphics

Features of Small Language Models


SLMs are easily accessible to a wide range of organizations and developers with minimum resource requirements. SLMs let individual researchers and small teams of organizations explore the potential of LA to save gauge models without prior language understanding and infrastructure investments.


SLMs are more efficient than LLMs regarding deploying and training. SLMs require less memory and computing power, making them fit for deployment on small devices. This feature unfolds the opportunities for many real-world applications, such as personalized mobile assistants and on-device chatbots. Businesses looking to minimize their cost of computing can operate on less powerful gears and require less training data, saving huge amounts of money.


Security is the primary concern in the deep learning process. SLMs introduce better security features than most of their larger counterparts. SLMs are fundamentally more secure as they have small codebases and fewer parameters, which reduce the possible attack surface for unauthorized users.


SLMs generate factually correct and precise information and are less susceptible to displaying biased results because of their small scale. SLMs can consistently produce accurate information by undergoing training on specific datasets.


Small language models typically display more transparent and explainable behavior than sophisticated LLMs. Because of its transparency, the model's decision-making process is easier to understand and audit, making it feasible to spot and fix security flaws.


SLMs are more accessible to fine-tuning for specific tasks and domains. This permits the creation of specialized foundation models customized to niche applications, leading to accurate and efficient performance.

How does SLM Work?

Like LLMs, SLMs are instructed on massive datasets of generative text and codes. Although, some techniques are employed to achieve efficiency and compact size. These techniques are:

  • Model Compression: Here, we take a pre-trained LLM and condense its essential features into a smaller model, maintaining its critical capabilities while streamlining its architecture.

  • Cropping and Quantization: These methods eliminate unnecessary components of the model and decrease the accuracy of its weights, resulting in a smaller size and reduced resource needs.

  • Innovative Designs for Additive Manufacturing: Researchers constantly explore new architectures tailored for Small Language Models, attempting to enhance efficiency and performance.

Some Examples of Small Language Models

Phi 2: A 2.7 billion-parameter language released by Microsoft that demonstrates brilliant reasoning and language understanding capabilities. Phi 2 is a transformed-based SLM designed for adaptability and efficiency in edge and cloud deployments. Microsoft's Phi-2 performs in common sense, mathematical reasoning, logical reasoning, and language understanding -

Orca 2: Another small language model developed by Microsoft is Orca 2, a result of fine-tuning Meta's Llama 2 using high-quality synthetic data. It is built for research purposes and returns a single-turn result in tasks like math problem-solving, reading comprehension, and text summarization. Microsoft's cutting-edge methodology allows for performance outputs that are on par with or even exceed those of more extensive models, particularly in tasks involving zero-shot reasoning -

DistilBERT: DistilBERT is a distilled, compact, agile, and lightweight iteration of BERT, a pioneer natural language processing (NLP) model. DistilBERT is a tight, quick, light, and cheap model trained by distilling the BERT base. Compared to bert-base-uncased, it has 40% fewer parameters and runs 65% faster, preserving 95% of BERT's performance as calculated on the GLUE language understanding benchmark -

BERT Mini, Small, Medium, and Tiny: Developed by Google, BERT is a Pytorch pre-trained model available in scaled-down versions - from Mini with 4.4 million parameters to Medium with 41 million parameters to fit in with multiple constraints -

MobileBERT: The MobileBERT was proposed for mobile devices. It is specifically designed to optimize mobile performance within some constraints. MobileBERT is a bidirectional transformer based on the model of BERT, accelerated and compressed with several approaches -

GPT-J and GPT-Neo: GPT-J and GPT-Neo are two scaled-down iterations of OpenAI's GPT model. It is a GPT-2-like casual language, offering versatility in various application scenarios with limited measurable resources. The architecture of GPT-Neo is similar to GPT-2 except that Neo uses local attention in every layer -

T-5 Small: T-5 Small is a part of Google's Text-to-Text Transfer Transformer (T5) model series, which bridges the balance between resource utilization and performance, leading to effective text processing capabilities -

Graph showing the SLMs models vs their number of parameters compared with other models

Applications of SLMs

Small language models can find applications in various domains like their larger counterparts. While they may not have the same scale and capacity for complex tasks as larger models, they can still be valuable in specific contexts. Here are some applications of small language models:

Text Generation for Chatbots: Small language models can generate responses for chatbots in customer support or information retrieval applications. Micro-models can effectively handle customer inquiries and routine problems, freeing up human effort to focus on other individual interactions.

Sentence Completion and Suggestions: They can assist users in completing sentences or providing suggestions while typing, enhancing the user experience in messaging apps and word processors. For example, Email Automation to compose emails and automate responses.

Sales and Marketing Optimization: SLMs are the best option for optimizing sales and marketing. It helps generate creative and engaging content for social media platforms, SEO Assistance, interactive storytelling, and personalized email campaigns. This lets businesses maximize their sales and marketing potential by sending more personalized and impactful messages.

Product Development Support: SLMs help in idea creation, testing of features, and customer demand forecasting, thus essential and applicable for product development.

Text-based Games: Small language models can create text-based games where the system responds dynamically to user input.

The Future of Small Language Models

Advancements in the research and development of SLMs ensure a more robust and versatile future. With continuous improvements in hardware advancements, training methods, and efficient architectures, the difference between LLMs and SLMs can be narrowed. This development will pave the way for innovative and disruptive uses of AI, making its power and potential more accessible to people from all walks of life.

The rise of small language models (SLMs) marks a significant turning point in artificial intelligence. Their streamlined design, adaptability, and user-friendly features make them an asset for developers and researchers across multiple industries. As SLMs continue to advance, they have the potential to unlock new possibilities for both individuals and organizations, creating a future where AI is not only powerful but also flexible and tailored to meet specific needs.


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