What are Small Language Models (SLM)?

In the world of artificial intelligence, there’s a lot of buzz around the big names—GPT-4, Gemini, Mixtral, and other massive models boasting billions of parameters. These large language models (LLMs) are the rockstars of the AI scene, capable of generating entire essays, writing code, and even holding complex conversations with humans. But as impressive as they are, they aren't always the right tool for the job. Sometimes, the task at hand requires not overwhelming power, but a more agile, efficient solution.

Before diving into the role of small language models (SLMs), it's worth acknowledging the giants of the field. Below are some logos of well-known LLMs, representing the cutting edge of AI technology.

Enter small language models (SLMs): the unsung heroes quietly working behind the scenes, getting the job done with far fewer resources. They may not have the glamor of their larger counterparts, but SLMs have carved out a critical niche in the AI ecosystem. And, in many cases, they’re the better choice when speed, efficiency, and resource constraints are key considerations.

The rise of SLMs#

You might be wondering: "Why would anyone want a small model when large models are clearly more powerful?" Just like in the world of smartphones, where smaller devices can sometimes outshine their bulkier counterparts, SLMs are all about being lightweight, efficient, and fast.

To truly appreciate the rise of SLMs, let's first understand their unique advantages. Here, we'll explore a scenario where SLMs outperform and why they’re essential for certain applications.

Imagine you’re developing an AI assistant for a mobile app. You want it to respond to user queries in real time, but the catch is that your app needs to run on devices with limited memory and processing power—think smartphones, tablets, or even embedded devices like smartwatches. This is where SLMs shine. They are smaller, quicker to train, and easier to deploy in low-resource environments.

While SLMs offer impressive efficiency and performance for many everyday tasks, we should not forget the the unique advantages that LLMs bring to the table. Understanding the distinct roles of both types of models helps clarify why LLMs continue to be essential in certain applications.

The need for LLMs#

However, it’s not all about small models. There are still critical use cases that demand the power and complexity of LLMs.

LLMs excel in handling complex, nuanced tasks, especially when understanding deep context or dealing with long-form text. They’re also better at multilingual applications and complex reasoning.

LLMs are necessary when you need:

• Complexity and depth: For tasks requiring deep context and sophisticated reasoning.

• Multilingual capability: When dealing with diverse languages, LLMs outperform smaller models.

• Transfer learning: LLMs encode vast knowledge from large datasets, making them better for handling outliers and uncommon tasks.

Why SLMs are the future#

While bigger models may have more raw power, SLMs can be far more efficient while being drastically smaller. Though LLMs are necessary for more complex tasks, SLMs are becoming the go-to option for real-time, resource-efficient applications. They are particularly beneficial for mobile and IoT devices, which require less memory and processing power. Fast deployment, real-time use cases (like chatbots or on-device assistants), and environmental concerns around energy consumption make SLMs a sustainable choice.

SLMs are all around us, quietly making our everyday tech experiences smoother and faster. Let’s take a look at where you might encounter them:

• Virtual assistants on mobile devices: Siri, Google Assistant, and Alexa often use small models when responding to commands. When you ask Siri to set a reminder or check the weather, an SLM may be processing that request on the fly, allowing for quick responses without needing to rely on a large, centralized model.

• Text autocompletion: When you’re texting on your smartphone and the keyboard suggests the next word, an SLM is likely behind the scenes, quickly predicting what you might want to say next. This requires a model that’s small and fast, because the system has to generate predictions in real time without lagging.

How to build a small language model#

Many SLMs are created through knowledge distillation, where a smaller model is trained to mimic the behavior of a larger one. The large model (teacher) provides more nuanced feedback than traditional labels, helping the small model (student) to perform efficiently without requiring as much computational power. This allows small models to deliver strong performance with fewer resources.

Ongoing research in the realm of SLMs is focused on enhancing their performance further while maintaining low resource consumption. Techniques like model pruning, quantization, and innovative architectures such as transformer-based models optimized for efficiency are at the forefront of this research. These advancements aim to push the boundaries of what SLMs can achieve without sacrificing speed or accessibility.

SLMs vs. LLMs#

How do SLMs really stack up against LLMs? To keep things simple, imagine a scenario where you’re trying to choose between using a large language model and a small one for a specific task. In the table below, a brief comparison is given:

A curious reader may raise the question: "If I open ChatGPT on my phone, doesn’t it act as an SLM?"

Here’s the difference: When you open ChatGPT (or any other LLM-based application) on your phone, it’s still powered by an LLM, not an SLM. While it might feel like the model is running directly on your device, the processing actually takes place on remote servers, typically hosted in the cloud. Your phone simply acts as an interface, sending inputs to these servers, which process them using the full LLM and return the response to your device.

Why not run it as an SLM directly on the phone? For tasks that require the subtle and exact understanding typical of LLMs, reducing the model down to an SLM would compromise quality. Even though SLMs are ideal for lightweight, quick-response tasks on mobile, they generally can’t handle the depth and complexity LLMs offer—particularly essential for applications like ChatGPT that are expected to deliver detailed, context-aware responses.

How SLMs work#

SLMs, as described above, are optimized versions of LLMs. They focus on fast inference, low computational costs, and suitability for low-resource environments. Let's have a look at the simplified process:

1. Input: A text prompt is provided.

2. Tokenization: The text is broken into tokens.

3. Embedding: Tokens are converted into numerical representations, or embeddings.

4. Processing: These embeddings are passed through the model’s neural network to generate a response.

5. Output: The model produces a prediction or text response.

6. Post-processing: The response is then detokenized and formatted.

Some SLMs and their characteristics#

LLMs form the backbone of SLMs designed for efficient operation on resource-constrained devices. Below, we examine some of the most notable parent LLMs and their core attributes, along with their SLMs.

BERT#

With 110 million parameters in the BERT-Base version, BERT excels at understanding contextual language, but its large parameter size can lead to higher latency, often above 200 ms per inference on standard hardware.

DistilBERT
DistilBERT, a streamlined version of the BERT model, reduces the number to 66 million. Despite this reduction, it retains over 95% of BERT’s original performance, making it highly efficient for a variety of NLP tasks. The model is designed to be much faster—running 60% quicker than BERT—while using significantly less memory. This makes DistilBERT ideal for situations where computational resources are limited but high accuracy is still required.

TinyBERT
Another derivative of BERT is TinyBERT, which is specifically optimized for mobile devices. With only 14.5 million parameters, TinyBERT is designed to deliver 96% of BERT’s performance while operating within the constraints of smaller hardware environments. TinyBERT’s compact size and high efficiency make it suitable for real-time applications such as chatbots and virtual assistants.

MobileBERT
MobileBERT takes BERT’s capabilities and tailors them for on-device computations, focusing on achieving BERT-level performance in environments with limited resources. With 25 million parameters, MobileBERT manages to achieve nearly 99% of BERT’s accuracy while being 4.3 times smaller and 5.5 times faster, making it a prime choice for mobile and IoT applications.

ALBERT
ALBERT is another variant of BERT that focuses on reducing model size through parameter sharing. With only 18 million parameters, ALBERT minimizes memory usage while maintaining a performance level comparable to BERT. This makes it ideal for tasks requiring lower resource consumption without significant compromises in accuracy.

GPT-4#

GPT-4, a powerful LLM by OpenAI, has an undisclosed but significantly large parameter count (considered to be in trillions), optimized for deep language comprehension and generation.

GPT-4o mini
GPT-4o mini is a compact version of the powerful GPT-4 model. Though the exact number of parameters remains undisclosed, this smaller version is designed to deliver around 85–90% of GPT-4’s full capabilities. It balances performance with resource efficiency, making it well-suited for real-time applications where the full capacity of GPT-4 isn’t necessary, but high-quality natural language generation is still needed.

Microsoft Phi#

Microsoft’s Phi model family, designed for high NLP performance, includes larger models with billions of parameters and incurs higher latency, making it more suitable for robust, server-based applications.

Microsoft Phi-3-mini
Microsoft Phi-3-mini is a more efficient version of Microsoft’s Phi model, containing 3.8B parameters. Despite its smaller size, Phi-Mini offers strong NLP performance, achieving around 90% of the accuracy of its larger counterpart. The reduced computational overhead makes this model appropriate for a wide range of applications, from real-time text generation to resource-constrained environments.

Llama#

The Llama-2 series from Meta, with its Llama-2-13B model containing 13 billion parameters, achieves strong performance in nuanced NLP tasks, though its latency can range between 80–100 ms per inference due to its substantial parameter size.

Llama-2-7B

Llama-2-7B, a smaller variant of the Llama-2-7B model family, contains 7 billion parameters. It delivers about 70–75% of the performance of the larger Llama-2-13B, making it an efficient choice for applications where computational resources are limited. The latency of Llama-2-7B is roughly 50–70 milliseconds per inference, compared to Llama-2-13B's 80–100 milliseconds. This efficiency makes the 7B model suitable for real-time natural language processing, especially in mobile and edge environments.

TinyLlama

TinyLlama is designed for efficient performance with substantially fewer parameters than its larger counterparts. With approximately 1.1 billion parameters, TinyLlama achieves around 60–65% of the performance of the Llama-2-7B model, offering a strong balance of capability and efficiency. Its latency is approximately 20–30 milliseconds per inference, making it exceptionally responsive and suitable for real-time natural language processing in resource-constrained environments like mobiles. TinyLlama is optimized for low-power deployments, providing a scalable solution for applications that require a small memory footprint without compromising utility.

Mistral AI#

The Mistral-13B model, with 13 billion parameters, is focused on high performance in natural language processing. Mistral's latency often ranges between 100–120 ms, positioning it as a suitable choice for applications with sufficient computing resources.

Mistral-7B

Mistral-7B, containing 7 billion parameters, delivers about 70–75% of the performance of the full Mistral-13B model. With this reduced size, Mistral-7B is highly efficient, offering a faster response rate (typically 40–60 milliseconds per inference) compared to its larger counterpart. It’s a versatile choice for applications that prioritize speed and accuracy on low-resource hardware.

Ministral-3B

Ministral-3B, with just 3 billion parameters, achieves around 60–65% of the original Mistral model’s performance. This even more compact version provides an optimal solution for devices with stringent memory and power constraints, making it an attractive option for embedded systems, IoT, and mobile-based NLP applications.

Challenges#

Despite their advantages, SLMs are not without limitations. Below are some key challenges faced by SLMs, along with an exploration of why these issues arise and potential ways to address them.

Limited context understanding#

SLMs, by design, have fewer parameters and smaller architectures than LLMs. This limits their ability to process and retain complex, long-range dependencies in text, which are often crucial for understanding nuanced or context-rich inputs. For example, they may struggle to connect a reference in a later sentence to a detail mentioned earlier in a paragraph.

Mitigation strategies:

• Context window optimization: Developers can expand the context window size of SLMs to allow for better context comprehension within a reasonable range.

• Task-specific pre-training: Fine-tuning SLMs on datasets specific to the task or domain can improve their ability to handle nuanced contexts.

• Hybrid models: Combining SLMs with larger models in a pipeline where the SLM handles basic tasks and the LLM is called for more complex understanding can balance efficiency and performance.

Reduced generative capabilities#

SLMs are typically trained with fewer parameters, which limits their ability to generate rich, creative, and detailed content. Their smaller capacity constrains their ability to capture the intricate relationships and stylistic nuances needed for tasks like storytelling, in-depth analysis, or sophisticated content creation.

Mitigation strategies:

• Post-processing tools: Pairing SLMs with post-processing algorithms or systems to refine their outputs can help generate more detailed content.

• Ensemble approaches: Using multiple SLMs focused on specific sub-tasks (e.g., grammar, structure, and style) can collaboratively enhance generative outputs.

• Knowledge distillation enhancements: Improving the distillation process to better capture generative knowledge from larger models can help reduce the performance gap.

Performance trade-offs#

SLMs optimize for speed and resource efficiency, which often means sacrificing some capability in specialized tasks or when dealing with outliers and rare events. Their smaller size restricts their ability to learn and generalize across all edge cases in training data.

Mitigation strategies:

• Specialized fine-tuning: Regularly fine-tuning SLMs on edge cases or domain-specific data can improve their performance on specialized tasks.

• Fallback systems: Integrating a fallback mechanism where a larger model is used only when the SLM encounters uncertainty or unusual inputs can ensure robustness.

• Dynamic scaling: Using adaptive algorithms that switch between SLMs and more powerful models based on task complexity can help balance performance and efficiency.

Knowledge retention#

During the knowledge distillation process, where an SLM is trained to mimic a larger "teacher" model, some nuanced or rare knowledge is inevitably lost. This is because the smaller model lacks the capacity to encode the breadth and depth of information stored in larger models.

Mitigation strategies:

• Selective distillation: Focus the distillation process on task-relevant knowledge, ensuring the smaller model retains critical information needed for its intended use.

• Periodic updates: Regularly retraining or fine-tuning the SLM with updated datasets can help address gaps in knowledge retention over time.

• Complementary external modules: Incorporating external knowledge bases or databases that the SLM can query for missing information can supplement its limitations.

Adaptability issues#

SLMs usually require additional fine-tuning to adapt to new domains or tasks due to their smaller capacity to generalize across diverse use cases. This can make them resource-intensive to retrain for specific applications, offsetting their initial efficiency advantages.

Mitigation strategies:

• Meta-learning: Employing meta-learning techniques to enable SLMs to quickly adapt to new tasks with minimal data can improve adaptability.

• Task-agnostic pre-training: Pre-training SLMs on a broader range of tasks or domains can improve their generalization capabilities, reducing the need for extensive fine-tuning.

• Reusable adapters: Leveraging lightweight adapter modules that can be swapped in and out for different tasks can minimize retraining overhead.

Multilingual limitations#

SLMs typically lack the extensive training datasets in multiple languages that LLMs are exposed to. Their smaller size also limits their ability to encode the complexities of linguistic diversity, such as syntax, semantics, and cultural nuances across languages.

Mitigation strategies:

• Targeted multilingual fine-tuning: Training SLMs on task-specific multilingual datasets can improve their performance in handling multiple languages.

• Transfer learning from LLMs: Using larger multilingual models to pre-train SLMs can transfer some of their linguistic capabilities to the smaller models.

• Language-specific models: Developing multiple smaller models tailored to specific languages or language groups can enhance their effectiveness for multilingual tasks.

Scalability concerns#

SLMs are inherently designed for smaller-scale applications, and their limited capacity becomes a bottleneck when scaling to larger datasets or handling increasingly complex tasks. They may fail to capture the breadth of information or relationships in expansive data, leading to reduced performance.

Mitigation strategies:

• Distributed architectures: Employing distributed systems that use multiple SLMs in parallel can help manage larger-scale tasks while maintaining efficiency.

• Modular pipelines: Structuring workflows into smaller, manageable sub-tasks, each handled by an SLM, can improve scalability without overburdening a single model.

• Incremental scaling: Gradually increasing the capacity of SLMs for specific use cases or integrating them with modular LLM components can ensure they remain effective in larger applications.

Future of SLMs#

As artificial intelligence continues to evolve, SLMs will grow in significance, particularly with the rise of edge computing—where AI runs directly on devices like smartphones, IoT devices, and wearables. This shift emphasizes the need for efficient AI models that don’t rely on cloud-based supercomputers. Models like TinyBERT, DistilBERT, and GPT-4o mini are excellent examples of how powerful SLMs can be when designed with efficiency and resource-consciousness in mind.

Interestingly, while LLMs like GPT-4 and Llama 3 are becoming more massive, there’s a counter-movement towards making AI models more accessible, lightweight, and sustainable. SLMs are crucial in this shift. For high-demand, low-latency AI applications such as augmented reality, real-time translation, and autonomous vehicles, SLMs offer optimal performance without the computational cost and energy consumption of larger models.

Additionally, SLMs will be vital for offline AI solutions and privacy-focused applications, enabling powerful AI to run locally on devices without an internet connection. This trend reflects the growing demand for efficient, fast, and scalable AI that integrates seamlessly into everyday technologies.

Conclusion and next steps#

While large language models often grab the headlines, small language models are quietly powering some of the most practical and ubiquitous AI applications around us. Their speed, efficiency, and lower resource demands make them the perfect fit for many real-world tasks, from running virtual assistants on mobile devices to handling everyday queries in chatbots.

So next time you’re asking Alexa for a weather update or texting with an auto-completing keyboard, remember that it’s probably an SLM doing the heavy lifting behind the scenes. And while it might not have the same star power as GPT-4, it’s every bit as important in keeping the world of AI running smoothly.

In the end, it’s not always about size. Sometimes, being small and efficient is exactly what you need.

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