For years, the AI industry focused mainly on large language models (LLMs), which require a lot of data and computing power to work. Small language models (SLMs) are now changing the game in AI. Unlike their bigger cousins, SLMs deliver similar results with much fewer resources.
Building on this trend, SLMs are really catching on in 2024. They don't need as much to run but still perform impressively, which solves many problems that LLMs couldn't. That's why they're becoming a popular choice in the industry, right alongside the larger models.
Sonali Yadav, Principal Product Manager for GenAI at Microsoft, explains the shift in the industry: "What we're going to start to see is not a shift from large to small, but a shift from a singular category of models to a portfolio of models where customers get the ability to make a decision on what is the best model for their scenario." This reflects the importance of using small and large models together rather than sticking solely to only one option.
In this article, we'll look at how SLMs stack up against larger models, how they work, their advantages, and how they can be customized for specific jobs. Let's see how these smaller models are making a big difference.
What are small language models (SLMs)?
Small language models (SLMs) are AI models designed to process and generate human language. They're called "small" because they have a relatively small number of parameters compared to large language models (LLMs) like GPT-3. This makes them lighter, more efficient, and more convenient for apps that don't have a ton of computing power or memory.
The best thing about small language models (SLMs) is that they work great even on simpler hardware, which means you can use them in lots of different settings. They're perfect if you don't need all the fancy features of a huge language model. Plus, you can fine-tune SLMs to do exactly what you need, making them really good for specific tasks. If your business is starting to play around with GenAI, SLMs can be set up quickly and easily.
How does an SLM work?
Small language models are designed to fit into smaller spaces, like your smartphone or a portable device, without sacrificing too much in the way of smarts. Let's go over how these compact models are built and what makes them so special.
Training techniques
There are two main techniques to train SLMs, or, in other words, turn larger models (LLMs) into smaller ones (SLMs). Those are LLM pruning and knowledge distillation (KD).
Knowledge distillation is one method used to train these models. Here, a smaller model, or the "student," learns from a bigger, already-trained model, the "teacher." This isn't about the student model gobbling up all the raw data it can get its hands on; instead, it learns smarter by focusing on refined insights from the teacher, which speeds up its learning process significantly.
Pruning is pretty straightforward. It's about getting rid of the extra bits that aren't really needed. By cutting out these excess parts, the model becomes faster and leaner, which is great when you need quick answers from your apps.
Quantization involves dialing down the detail in the data the models handle. This doesn't mean the models become less accurate, but they do become quicker and lighter, which is a plus for running them on less powerful devices.
Model architecture
SLM architecture uses transformer technology—the heart and soul of GenAI. We'll break down the setup in simpler terms so as not to get too detailed in the technical stuff.
This setup includes:
- Self-attention mechanisms that help the model figure out which parts of the data are important and which can be ignored. It's like having a built-in filter that helps focus only on what's necessary.
- Feedforward neural networks process this filtered information quickly and efficiently, ensuring that the model doesn't get bogged down.
- Layer normalization is used to keep everything running smoothly, making sure that the model's outputs are consistent and reliable.
Using SLMs
Things get interesting once the SLM training is over. Turns out, these models are pretty flexible:
- Fine-tuning SLMs involves training the model for specific tasks. This could mean training it a bit more on specialized data so it can handle particular requests, like understanding medical terms or recognizing different accents in speech. We'll discuss this in more detail later in the article since SLMs with fine-tuning is a very juicy topic.
- Inference is when the model is actually put to work, and thanks to all the streamlining, it can deliver quick and reliable results. This makes them perfect for real-time applications like translating languages on the go or helping navigate menus in a new app.
SLMs vs. LLMs
We've reached a very important point—comparing SLMs with LLMs. Which one is better? When should you choose the larger models over the smaller ones? Can you achieve high performance with our tiny models? Let's find out for each metric.
Size: LLMs like Claude 3 and Olympus come with about 2 trillion parameters. Meanwhile, smaller models like Phi-2 only have 2.7 billion parameters. Despite this, Phi-2 has shown strong skills in areas like math and coding, sometimes even outperforming models that are much larger. For instance, Phi-2 has done better than the Llama-2-70B model in tasks that require multi-step reasoning, showing that smaller models can still deliver excellent results.
Training data: LLMs such as GPT-4 need a broad range of data, from books to websites, to produce detailed and nuanced text. On the other hand, SLMs like Phi-2 focus on high-quality, specific data, including 1.4 trillion tokens from both synthetic datasets and select web content.
Training time: Training a large model like GPT-3 can take several months and requires lots of computing power, usually involving many powerful GPUs. Phi-2, however, was trained in just 14 days on 96 A100 GPUs. This shows that SLMs can be developed much faster, which is beneficial for organizations that need to quickly iterate on their models.
Computing power and resources: LLMs like GPT-4 require significant computer power and memory, which can make them expensive to run. SLMs, however, can run efficiently on standard hardware, making them more accessible for a wider range of applications and easier on your budget.
Proficiency: While LLMs are good at handling a wide variety of complex tasks—from creative writing to detailed analyses or translating languages—SLMs are particularly good at specific tasks such as coding and reasoning. In fact, Phi-2 has achieved a top score of 53.7 on the HumanEval benchmark for coding, surpassing many larger models.
Adaptation: Adapting large models like BERT to specific needs can require significant effort and time. In contrast, smaller models like TinyBERT can be quickly fine-tuned for specific tasks such as sentiment analysis, making them more flexible and easier to customize.
Inference: Large models need strong hardware and often cloud services to operate, which means they rely on an internet connection. Phi-2 is compact enough to run on smaller devices like a Raspberry Pi or even a smartphone, offering more flexibility since it doesn't require internet access.
Latency: If you've tried using a large model for something like a voice assistant, you might have noticed a delay. SLMs, being smaller, can process requests much faster, which improves user experience in real-time applications.
Cost: Running large models can be costly because they need a lot of computing resources. Since SLMs don't need as much power, they are cheaper to operate, which can be a major advantage for organizations looking to save on costs.
Control: Using large models means relying on their developers for updates, which can lead to problems like model drift. With Phi-2 and other small models, you can run them on your own servers, fine-tune them to your needs, and keep them consistent over time. This gives you more control and can be critical for businesses that value data privacy and model reliability.
The role of data quality in SLMs
It's like the old saying, "You are what you eat." For AI, it's "You perform as well as the data you learn from." If the data's good, you can even take a basic starting model like GPT-2 and turn it into something special by training it on really standout data sets.
Data quality really matters, especially for small language models (SLMs). Since these models aren't as big or complex as the large ones, they rely heavily on the quality of data they're trained on to perform well.
Take Microsoft's TinyStories dataset, for example. It's specifically designed for writing children's stories and uses just about 3,000 words. Because the data is so focused and clean, small models trained on it can actually write pretty good stories that make sense and stick to proper grammar. It's a clear case of less being more when the less is really good.
This idea holds up in other specialized areas, too. If you're working with legal texts, a model trained on a bunch of legal documents is going to do a much better job than one that's been learning from random internet pages. The same goes for healthcare—models trained on accurate medical information can really help doctors make better decisions because they're getting suggestions that are informed by reliable data.
Being able to quickly adjust these models to new tasks is one of their big advantages. Say a business has an SLM running their customer service chat; if they suddenly need it to handle questions about a new product, they can do that relatively easily if the model's been trained on flexible, high-quality data. And remember - with lower computational costs and power.
So yeah, the kind of data these small models train on can make or break them. That's why anyone using them needs to make sure they're feeding their AI the good stuff—not just a lot of it, but high-quality, well-chosen data that fits the task at hand. That's why you should trust building your data with reliable experts. We'll talk about this in the next section.
SLM fine-tuning at SuperAnnotate
Getting your SLM just right depends on the quality of your training data. That's where SuperAnnotate comes into play, helping businesses build high-quality datasets that are crucial for fine-tuning language models to meet specific needs.
Fine-tuning is really about refining your model's abilities for particular tasks. SuperAnnotate is at the top of this process, helping companies customize their SLMs and LLMs for unique requirements. Say a business needs its model to grasp industry-specific jargon—SuperAnnotate is there to build a dataset enriched with all the necessary terms and their contexts.
Our collaboration with Databricks highlights the impact of quality data. Jonathan Frankle from Databricks shared, "We selected SuperAnnotate due to the high quality of their data... They are an invaluable part of our data pipeline." This feedback marks the importance of having precisely curated data for successful model fine-tuning.
Coupled with easy integration into platforms like IBM WatsonX and Snowflake, the entire fine-tuning process becomes seamless. Users can gather data, adjust their models, and evaluate outcomes using tailored metrics, simplifying and enhancing the workflow.
If you're interested in seeing how SuperAnnotate can help fine-tune your language model, feel free to request a demo.
SLM benefits
We talked about how SLMs compare with LLMs, but let's also talk about where they really shine and why they’re one of the main GenAI trends in 2024.
- Accessible and affordable: SLMs will save you money and they're easy to get your hands on. This makes them a great choice for both developers and small businesses that want to try AI.
- More energy-efficient: SLMs are also kind to the planet. They use less energy, which means they're better for the environment—a win if you're looking to green your tech.
- Valuable for educational purposes: These models are really handy in the classroom. They can help with everything from language learning to homework help, making them a valuable tool for educators and students alike.
- Cheaper to develop: It's less expensive to create and maintain SLMs because they need less data and computing power. This means you can innovate without spending a fortune.
- Easier to customize: As we mentioned, tailoring an SLM to fit specific needs is easy. Whether you're a startup or a researcher, this makes them perfect for specialized projects where customization is key.
Small language model examples
Microsoft, Mistral, Meta—these are the big names behind small language models. Microsoft led the way with its Phi-3 models, proving that you can achieve good results with modest resources.
Now, let’s explore some of the most famous small language models.
- Mixtral: Customized efficiency
Mixtral's models – Mixtral 8x7B, Mixtral 7B, Mistral small – optimize their performance with a 'mixture of experts' method, using just a portion of their parameters for each specific task. This makes it capable of handling complex tasks efficiently, even on regular computers.
- Llama 3: Enhanced text comprehension
Meta's Llama 3 can understand twice as much text as its earlier version, enabling deeper interactions. It uses a larger dataset to boost its ability to tackle complex tasks and is seamlessly integrated across Meta's platforms, making AI insights more accessible to users.
- Phi-3: Small but mighty
Microsoft's Phi-3-mini works with only 3.8 billion parameters. It's great at summarizing detailed documents, running customer support chatbots, and crafting marketing content. Plus, it meets Microsoft's high standards for privacy and inclusivity.
- DeepSeek-Coder-V2: Coding companion
With strong coding and math skills, DeepSeek-Coder-V2 is like having an additional developer right on your device. It's perfect for coders who need a reliable helper that can stand on its own.
- MiniCPM-Llama3-V 2.5: Versatile and multilingual
MiniCPM-Llama3-V 2.5 is adept at handling multiple languages and excels in optical character recognition. Designed for mobile devices, it offers fast, efficient service and keeps your data private.
- OpenELM: Private and powerful
Apple's OpenELM is a line of compact AI models designed for use right on your device, ranging from 270 million to 3 billion parameters. They work locally to keep your data secure and your processes quick—no cloud needed.
- Gemma 2: Conversation expert
Gemma 2 improves on its predecessor by balancing compact size with strong performance. It shines in conversational AI, making it ideal for applications that require swift and precise language processing.
Closing remarks
To sum it up, small language models are making a big impact in AI. They're affordable, practical, and fit well into many business needs without the need for supercomputers. These models are great for a range of tasks—from customer service to number crunching and even educational applications—all without the heavy resource use that bigger models often require.
As technology progresses, small language models are only going to become more important. They give businesses of all sizes a more manageable way to tap into the benefits of AI, paving the way for smarter and more efficient solutions across industries.
