Small Language Models: A Big Leap for AI on a Smaller Scale

Sometimes, smaller models can be the smarter choice when it comes to generative AI. For many businesses, small language models (SLMs) that are specifically designed for focused tasks often prove to be more efficient and practical than large, general-purpose models.?

Although these models may not match the raw power of larger models, their efficiency and adaptability make them an increasingly important tool in AI. They provide a solution when larger models would be overkill or impractical due to hardware limitations.

Let’s discuss why SLMs are gaining popularity, how they work, their use cases in AI, and how they could really benefit.?

What are Small Language Models?

A small language model (SLM) is basically a scaled-down version of larger language models (LLMs). It’s a natural language processing (NLP) model designed to handle many of the same language-related tasks but with fewer parameters, which means it doesn’t need as much computing power or memory.?

This makes SLMs ideal for situations where resources are limited, like smaller businesses or devices with less processing capacity.?

What makes SLMs particularly valuable is their accessibility. By offering open-source models, they democratize AI technology, allowing a broader audience – researchers, industries, and developers – to implement NLP solutions without needing the high-end systems typically required by larger models. This translates to lower costs and more practical, affordable AI for everyday use.

How Small Language Models Work

Many SLMs are built on the transformer architecture, which uses self-attention mechanisms for processing data efficiently. However, SLMs feature fewer layers and parameters, making them faster and more resource-efficient.

Model Distillation

One common method for creating SLMs is model distillation, in which a smaller model (the “student”) learns from a larger pre-trained model (the “teacher”). This results in compact models that retain high performance with fewer parameters.?

Additionally, techniques like parameter sharing and factorization help further reduce model size.

Training Stages

SLMs undergo two primary training stages:?

1. Pre-training: During pre-training, models learn general language representations from large datasets,
2. Fine-tuning: In fine-tuning, they adapt to specific tasks like text classification, making them highly versatile.?

Optimization techniques such as learning rate scheduling and mixed precision training improve efficiency and performance, while regularization prevents overfitting.

With lower computational demands, faster training, and adaptability, SLMs are ideal for tasks in embedded systems, mobile apps, and other platforms where LLMs are impractical.

Small Language Models vs. Large Language Models

Beyond their size, SLMs and LLMs differ in several major areas. SLMs excel at niche, domain-specific tasks and can offer more precise, expert information. For instance, in specialized fields like finance or healthcare, an SLM can be trained on industry-specific terminology and provide detailed outputs, making it a powerful tool for businesses with specialized needs.

In contrast, LLMs are more generalist, trained on vast datasets spanning multiple disciplines. While this makes them versatile, they may struggle to provide the same level of precision in industry-specific contexts as SLMs. An SLM tailored to the domain may outperform an LLM in scenarios requiring deep, specialized knowledge.

SLMs also offer practical advantages due to their compact size. They can be easily deployed on mobile devices and edge computing platforms, providing fast, lightweight solutions. On the other hand, LLMs require significant computational power to operate, making them harder to implement in low-resource environments.

Finally, regarding privacy and security, SLMs often have an edge. Since they can be run locally, sending data to external servers is unnecessary, reducing the risk of data breaches. LLMs, however, frequently rely on cloud-based operations, which may expose sensitive data to potential security risks.

5 Small Language Model Examples and Use Cases

As the demand for generative AI grows, the variety of SMLs continues to expand, driven by data scientists and developers exploring new small language models’ use cases in AI.?

One of the most well-known and widely used is the open-source BERT language model and its various iterations. These models are flexible in size, making them suitable for a range of different deployment scenarios.

Now, let’s take a closer look at some of the standout small language models, breaking down their main features and how they’re used.

1. PHI-3 – Small but Powerful

Microsoft revealed its new PHI-3 family of open models in April 2024, describing them as “the most capable and cost-effective SMLs out there.” The first model, Phi-3-mini, has 3.8 billion parameters but claims to outperform models twice its size.

This compact model is great for tasks like:

• Summarizing long, detailed documents like new regulations.
• Powering chatbots that give accurate customer support and personalized recommendations.
• Creating marketing content like social media posts or product descriptions.

Microsoft says the PHI-3 models follow strict responsible AI standards, ensuring they are secure, private, and reliable.

If Microsoft delivers on its promises, the PHI-3 family could become a top contender in SMLs.

Llama 3 – A Powerful Language Model for Computer

Meta’s Llama 3 is a significant improvement over the previous version, Llama 2. It was trained on a seven times larger dataset with four times more code, making it much more powerful.

One major upgrade is that Llama 3 can handle up to 8,000 tokens of text, which is double what Llama 2 could manage. This makes it great for generating longer and more complex content.

Llama 3 also has stronger reasoning skills, ranking among the best open-source AI models based on industry tests. Meta plans to use it to push AI innovation forward, impacting how apps are developed and improved.

Llama 3 is already part of Meta’s products, such as WhatsApp, Facebook, Messenger, and Instagram, helping with searches, feeds, and chats under the Meta AI brand.

Mixtral of Experts – A Smarter Mix for Enhanced Reasoning

Mixtral, created by Mistral AI, is a highly efficient small language model. It works by selecting the best set of parameters (from eight sets) for each part of a text using a special neural network known as a router. While it has 46.7 billion parameters in total, it only uses 12.9 billion at any given moment to process text.

What makes Mixtral stand out is its ability to handle complex tasks like large language models (LLMs), but in a more cost-effective and resource-efficient way. It’s trained on web data and uses the router to guide it, ensuring expert knowledge is applied where needed.

Compared to ChatGPT 3.5, here’s how Mixtral measures up:

• It pulls from various knowledge domains for a broader understanding.
• Its design allows it to perform like an LLM while using fewer resources.
• Mixtral can run on local devices, offering performance similar to much larger models.

DeepSeek-Coder-V2 – Extra Developer on Hand

Thanks to its impressive coding and problem-solving skills, DeepSeek-Coder-V2 can be considered an “extra developer.” This AI tool stands out among smaller language models designed for generating code. When used on a local machine, it is a solid alternative to tools like Copilot or Gemini Code.

DeepSeek-Coder-V2 is open-source and built using the Mixture-of-Experts (MoE) machine learning method. It’s pre-trained with six trillion tokens, supports 338 programming languages, and has a context length of up to 128k tokens. Coding tasks are performed on par with GPT4-Turbo.

It achieved a 90.2% success rate on the HumanEval benchmark in tests, showcasing its top-tier accuracy.

Who would benefit most? Businesses needing high-level analysis in their small language model (SLM) and those who prefer to keep their code secure on local systems reduce the risks associated with cloud-based tools.

MiniCPM-Llama3-V 2.5 – A GPT-4V-Level Multimodal LLM for the Phone

The MiniCPM-Llama3-V 2.5 is a powerful new model in the MiniCPM-V series with eight billion parameters, making it much better than the earlier MiniCPM-V 2.0. Even though it has fewer parameters than some larger models, it scored an impressive 65.1 on OpenCompass, beating models like Gemini Pro, GPT-4V-1106, Qwen-VL-Max, and Claude 3.

One of its best features is its low error rate, or “hallucination rate,” of only 10.3% in a test called Object HalBench. This makes it more reliable than GPT-4V-1106, which had a higher error rate of 13.6%.

This model works with more than 30 languages and is great for optical character recognition (OCR), which helps it read and process images with up to 1.8 million pixels. It scored 700 on the OCRBench test, doing better than Gemini Pro and GPT-4o.

MiniCPM-Llama3-V 2.5 is perfect for tasks like:

• Digitizing printed and handwritten text,
• Extracting data, such as converting tables into easy-to-read formats.

Additionally, MiniCPM-Llama3-V 2.5 can be used on mobile devices, ensuring that image data remains on the phone, thus enhancing privacy and data security.

What are the Limitations of Small Language Models

A small language models’ limitation is that the text they generate isn’t as smooth, varied, or understandable as what LLMs can produce. This happens because SLMs learn from smaller pieces of data, so they don’t pick up as many patterns or ways to structure sentences.

SLMs also struggle with handling different types of tasks as easily as LLMs. Large models are better at dealing with a wide range of tasks and topics, thanks to their bigger size and ability to “learn” from more data.

Another limitation is how well SLMs can adapt to new tasks. For example, large models excel in transfer learning, which means using knowledge from one task to help with another, and few-shot learning, where models learn new skills with very little data.?

In contrast, SLMs often need more data and extra fine-tuning to perform well on new tasks, making them less adaptable.

Small Language Models: Key Takeaways

While large language models dominate the AI scene with their impressive capabilities, small language models provide a practical, efficient alternative for many businesses and applications.?

Though they may lack the immense power of their larger counterparts, SLMs excel in niche areas where large models might be unnecessary or impractical. They are perfect for embedded systems, mobile applications, and situations where computational resources are limited.?

They offer several key advantages:?

• They can be deployed faster,
• They adapt easily to specific tasks,?
• They offer more secure, energy-efficient, and cost-effective solutions.

As technology advances, the role of SLMs will continue to grow, proving that sometimes, smaller is indeed smarter.

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