Top 5 small language models and their best use cases

Remember all the buzz about huge AI models, the ones with billions or even trillions of parameters? Those large language models (LLMs) are super powerful and can do a ton of different things. But honestly, they aren’t always the perfect tool for every single job.

Turns out, there’s another kind of AI that’s really making waves: the small language model (SLM). These models are showing us that you don’t necessarily need massive scale to get impressive results, especially when you’re focusing on specific tasks.

In this post, we’re going to dive into the world of SLMs. We’ll chat about what they are, how they stack up against their bigger cousins, and why businesses are starting to pay serious attention to them. Plus, we’ll highlight five cool small language models and look at how people are actually using them in impactful ways. You’ll see how being efficient and focused is opening up new doors for AI, and how platforms like eesel AI are using this tech to offer practical, powerful AI help for everyday business stuff, like customer support.

What are small language models?

Think of small language models (SLMs) kind of like the specialists in the AI world. If LLMs are generalists who know a little about everything, SLMs are the experts in a particular area.

At their heart, SLMs are AI models built to understand, process, and create human language. The main difference, as the name gives away, is their size.

While large language models might have hundreds of billions or even trillions of parameters (those are like the internal knobs and dials they adjust during training), SLMs typically fall somewhere between a few million and a few billion parameters. This smaller size means they don’t need nearly as much memory or computing power to run. Often, SLMs are actually created by taking a larger model and shrinking it down using clever techniques. This could involve teaching a smaller model to act like a bigger one (that’s called knowledge distillation), trimming away less important parts (pruning), or making the data less precise but faster to process (quantization). The goal is to make them leaner while keeping a lot of their punch for specific tasks.

Small language models vs large language models

It’s not just about the parameter count. There are some really important differences between small and large language models that make each one a better fit for different situations.

LLMs get trained on enormous, super-varied datasets pulled from all over the internet. This makes them incredibly flexible and able to handle a huge range of open-ended tasks, from writing creative stories to tackling complex problems across lots of different topics. But sometimes, because they’re so general, they might not be super accurate on really specific, niche subjects. They can also sometimes sound very convincing while saying something completely wrong (that’s what people mean by “hallucinations”).

SLMs, on the other hand, are often trained or fine-tuned using smaller, but higher-quality datasets that are specific to a certain area. This focus lets them perform just as well, or even better, than LLMs on tasks within that specific domain. Because they’re smaller, SLMs are way more efficient. They need less expensive hardware and use less energy. This makes them awesome for running in places where resources are tight, like on your phone or on small devices (edge hardware). They can also run on your company’s own servers (on-premises), which can be a big plus for data privacy and security compared to relying only on huge cloud-based LLMs. Their smaller size also means they respond faster, which is crucial for things that need real-time answers.

Here’s a quick side-by-side look:

Why small language models matter for businesses

The cool things about small language models translate into some pretty big wins for businesses looking to use AI effectively.

• Saving money: With lower computing needs, you spend less on hardware, electricity, and cloud services. This makes advanced AI more accessible, even for smaller teams and startups that find large models too costly.
• Speed and quick responses: Faster response times make SLMs ideal for real-time tasks like customer service chatbots, delivering quicker and smoother experiences for users.
• Better privacy and security: Running SLMs on your own servers or private cloud gives you greater control over data. This is crucial for industries like healthcare or finance that handle sensitive information.
• Easy customization: SLMs are easier and faster to fine-tune with your specific data. This means more accurate and on-brand outputs that reflect your company’s language and style. Platforms like eesel AI make this kind of customization simple and effective.
• Flexible deployment: Their ability to run on less powerful devices means you can use AI on phones, small gadgets, or even offline, expanding where and how you deliver AI-powered support.
• More sustainable: Using much less energy than larger models, SLMs help reduce your carbon footprint, which is a big plus for businesses focused on sustainability.

Top 5 small language models and their use cases

The world of small language models is moving fast, with new ones popping up all the time. But some have really stood out because of how well they perform, how efficient they are, or what unique things they can do. These models show just how much potential SLMs have for different kinds of tasks.

Let’s check out some of the leading small language models making noise and how businesses are actually putting them to work.

1. eesel AI (Platform leveraging SLMs/LLMs)

Okay, first off, it’s good to know that eesel AI isn’t a foundational language model itself, like the others we’ll talk about. Instead, eesel AI is a platform that uses powerful language models, including smaller, optimized ones when they’re the right fit, to create really effective AI support agents and copilots. This setup lets businesses tap into the benefits of advanced AI, like the efficiency and specialization you get from SLMs, without having to train or manage complicated models themselves.

Why it’s on the list: eesel AI takes the power of optimized language models and makes it practical and easy to use for specific, high-impact business jobs like automating customer support. It’s a great example of how efficient AI can be used in the real world to get tangible results for your business.

Use Cases:

• Handling tickets automatically: It can instantly solve those basic, repetitive customer questions that flood helpdesks like Zendesk or Freshdesk.
• Smart ticket sorting: Automatically categorizing and tagging support tickets based on what they say, what the customer needs, and how urgent it is.
• Helping human agents: Giving your support team draft replies, quickly finding information, and suggesting things based on the conversation context, often through a browser extension.
• E-commerce help: Pulling up details like order tracking or customer-specific info from platforms like Shopify to answer common online shopping questions.
• Doing custom actions via APIs: It can perform more advanced tasks like processing refunds or updating customer accounts by connecting directly to your internal systems using APIs.

You can actually see how eesel AI hooks up to your existing tools and learns from your specific company data right from their dashboard. To learn more, just head over to the eesel AI website.

2. Phi-3 (Microsoft)

Microsoft’s Phi-3 family of models is a fantastic example of getting impressive performance from a relatively small size. People often call them “tiny but mighty.” Models like Phi-3-mini (which has 3.8 billion parameters) have shown they can do really well on tests for reasoning and understanding language, sometimes even better than models twice their size. They think this is because they were trained on really high-quality, carefully chosen data.

Use Cases:

• Summarizing documents: Quickly creating summaries of long, complicated, or specialized documents, like legal papers or research reports.
• Powering chatbots: Running accurate and fast customer service chatbots that you can put on your website or in apps, potentially linking up with systems like your CRM.
• Creating content: Helping out with writing different kinds of content, from marketing stuff to product descriptions or internal company messages.
• AI on Your cevice: Their small size means you can put them on mobile phones, allowing for AI features that work even when you’re offline, like analyzing text or summarizing on the go.

3. Llama 3 (Meta)

Meta’s Llama 3 is a well-known open-source language model family that includes smaller, more accessible versions, like Llama 3.2 with 1 billion and 3 billion parameters. It was trained on a huge amount of data and shows improved reasoning skills and strong performance on various language tasks, making it a solid base model to start with.

Use Cases:

• Understanding & writing text: It’s great at understanding and creating longer and more complex pieces of writing. This is useful for things like creating content, analyzing documents, or building systems that have conversations.
• Getting Info in real-time: It’s integrated into Meta AI across apps like Instagram and WhatsApp to give users instant answers and information without them having to leave the app.
• Summarizing: It can summarize conversations, articles, or documents, with the smaller versions designed to run smoothly even on mobile phones.
• Customizing: Since it’s open-source, Llama 3 is a popular pick for developers and businesses who want to take a powerful base model and fine-tune it for their specific area or task.

4. Mixtral (Mistral AI)

Mistral AI’s Mixtral models, like Mixtral 8x7B, use a cool setup called a “mixture of experts” (MoE). Even though the model might have billions of parameters in total, it only uses a part of them (like 12.9 billion for Mixtral 8x7B) for any given task or piece of text it’s processing. This lets it handle complicated jobs really efficiently, sometimes as well as much bigger, traditional models like GPT-3.5, but without needing nearly as much computing power when it’s running.

Use Cases:

• Handling complex tasks: It’s good at jobs that need it to pull knowledge from different areas, making it suitable for questions that are a bit more involved than what simpler SLMs can handle.
• Easier to deploy: The MoE structure means it can run efficiently on hardware that’s less powerful than what traditional large models need, making it more accessible to get up and running.
• Better reasoning: Its design helps it with logical thinking and analysis, giving it advanced capabilities for a smaller model size.

5. DeepSeek-Coder-V2 (DeepSeek AI)

If you’re looking specifically at tasks related to writing software, DeepSeek-Coder-V2 is a seriously capable small language model. This is another MoE model, and it’s specifically built and trained on a massive 6 trillion tokens of data, focusing heavily on coding and math. It can handle a huge amount of text at once (a 128k context length), which is great for working with large codebases.

Use Cases:

• Writing code: It can help developers by generating bits of code, functions, or even bigger chunks of code just from descriptions in plain language.
• Explaining & translating code: It can understand existing code and explain it in simple terms, or even translate code from one programming language to another.
• Automated code checks: It can potentially help find bugs, suggest ways to make code better, or check if code follows your team’s style rules.
• Secure coding locally: Since it can run on your own computer, it’s a strong option for coding tasks that involve sensitive information where keeping data private is absolutely critical.

Comparing the top small language models

Each of these small language models brings its own strengths to the table. They’re often optimized for different kinds of tasks or where you plan to run them. While eesel AI is a platform that uses models, the foundational models listed here have distinct abilities.

Choosing the right small language model for your needs

Picking the best small language model isn’t really about finding the single best one out there. It’s more about finding the best fit for your specific needs. Since SLMs are often specialized, what you plan to use it for is the most important thing to think about.

• Define your task: What problem are you solving? Are you automating emails, building an internal bot, or writing code? Knowing this helps you choose the right model.
• Consider your resources: Where will it run? If it needs to work on a phone or small device, you’ll need a truly efficient model. With access to stronger servers or private cloud, larger SLMs or MoE models might work.
• Check your data: Do you have strong, specific data? If so, choose a model or platform that supports easy fine-tuning with your information.
• Prioritize privacy: If you handle sensitive data, running an SLM on your own servers or private cloud is often safer than relying on public cloud models.
• Think about integration: How will it connect with your tools like helpdesks or CRMs? Some models and platforms, like eesel AI, make this easy with built-in connections.
• Understand costs: Check how pricing works. Is it based on usage, agents, or computing power? Make sure it scales predictably with your growth.

How small language models power smarter support agents

While basic SLMs can understand language, platforms built for support, like eesel AI, take it further. They automate workflows, connect with your existing tools, and allow full customization. eesel AI makes optimized language models practical for customer support by handling data training, integrating with helpdesks like Zendesk and Freshdesk, and performing complex actions that standalone models can’t.

Ready to leverage small language models for your support?

Small language models make powerful AI more accessible, efficient, and specialized. They offer speed, lower costs, better privacy, and easy customization for specific tasks. In many cases, they are even better than, or a strong complement to, large language models.

Choosing the right model or platform depends on your goals, available resources, and privacy needs. Whether you need AI for devices, coding, or business applications like customer support, there is an SLM or a platform that fits.

Imagine putting this power to work for your support team. That’s exactly what eesel AI is built to do. It uses optimized language models to create AI Agents and Assistants that automate and streamline support workflows. You can easily connect eesel AI with helpdesks like Zendesk, Freshdesk, or Intercom, teach it using your company data, set the tone, and define actions. Plus, pricing is based on interactions, not per agent.

See how eesel AI can automatically handle common Tier 1 tickets, support your team, and lower costs using efficient, tailored AI.

Learn more on the eesel AI website, book a demo, or start a free trial today to experience it for yourself.