Small Language Models (SLMs): A Comprehensive Overview

The past few years have been a blast for artificial intelligence, with large language models (LLMs) stunning everyone with their capabilities and powering everything from chatbots to code assistants. However, not all applications demand the massive size and complexity of LLMs, the computational power required makes them impractical for many use cases. This is why Small Language Models (SLMs) entered the scene to make powerful AI models more accessible by shrinking in size.

Let's go through what SLMs are, how they are made small, their benefits and limitations, real-world use cases, and how they can be used on mobile and desktop devices.

What are Small Language Models?

Small Language Models (SLMs) are lightweight versions of traditional language models designed to operate efficiently on resource-constrained environments such as smartphones, embedded systems, or low-power computers. While large language models have hundreds of billions—or even trillions—of parameters, SLMs typically range from 1 million to 10 billion parameters. The small language models are significantly smaller but they still retain core NLP capabilities like text generation, summarization, translation, and question-answering.

Some practitioners don't like the term "Small Language Model", because a billion parameter is not small by any means. They prefer "Small Large Language Model", which sounds convoluted. But the majority went with Small Language Model, so SLM it is. By the way, note that it is only small in comparison with the large models.

How Are They Made Small?

The process of shrinking a language model involves several techniques aimed at reducing its size without compromising too much on performance:

1. Knowledge Distillation: Training a smaller "student" model using knowledge transferred from a larger "teacher" model.
2. Pruning: Removing redundant or less important parameters within the neural network architecture.
3. Quantization: Reducing the precision of numerical values used in calculations (e.g. converting floating-point numbers to integers).

Examples of Small Language Models

Several small yet powerful language models have emerged, proving that size isn’t everything. The following examples are SLMs ranging from 1-4 billion parameters:

• Llama3.2-1B – A Meta-developed 1-billion-parameter variant optimized for edge devices.
• Qwen2.5-1.5B – A model from Alibaba designed for multilingual applications with 1.5 billion parameters.
• SmolLM2-1.7 – From HuggingFaceTB, a state-of-the-art "small" (1.7 billion-parameter) language model trained on specialized open datasets (FineMath, Stack-Edu, and SmolTalk).
• Gemma-2-2B – Developed by Google DeepMind, this 2 billion-parameter model balances performance and efficiency; it also scores high in writing and safe classification.
• Phi-3.5 Mini – Microsoft's tiny-but-might open model with 3.8 billion-parameters optimized for reasoning and code generation.

Here are other more powerful small language models available out there: Mistral 7B, Gemma 9B, and Phi-4 14B (though I'm not sure if Phi-4 with 14 Billion parameters still qualifies as "small" but it's so damn capable :)

Benefits of Small Language Models

• Low Compute Requirements – Can run on consumer laptops, edge devices, and mobile phones.
• Lower Energy Consumption – Efficient models reduce power usage, making them environmentally friendly.
• Faster Inference – Smaller models generate responses quickly, ideal for real-time applications.
• On-Device AI – No need for an internet connection or cloud services, enhancing privacy and security.
• Cheaper Deployment – Lower hardware and cloud costs make AI more accessible to startups and developers.
• Customizability: Easily fine-tuned for domain-specific tasks (e.g., legal document analysis).

Limitations of Small Language Models

While SLMs offer numerous advantages, they also come with certain trade-offs:

• Narrow Scope: Limited generalization outside their training domain (e.g., a medical SLM struggles with coding).
• Bias Risks: Smaller datasets may amplify biases if not carefully curated.
• Reduced Complexity: Smaller models may struggle with highly nuanced or complex tasks that require deep contextual understanding.
• Less Robustness: They are more prone to errors in ambiguous scenarios or when faced with adversarial inputs.

Real-World Applications of Small Language Models

Despite their limitations, SLMs have a broad range of practical applications:

1. Chatbots & Virtual Assistants: Efficient enough to run on mobile devices while providing real-time interaction.
2. Code Generation: Models like Phi-3.5 Mini assist developers in writing and debugging code.
3. Language Translation: Lightweight models can provide on-device translation for travelers.
4. Summarization & Content Generation: Businesses use SLMs for generating marketing copy, social media posts, and reports.
5. Healthcare Applications: On-device AI for symptom checking and medical research.
6. IoT & Edge Computing: Running AI on smart home devices without cloud dependency.
7. Educational Tools: Tutoring systems can utilize SLMs to generate personalized explanations, quizzes, and feedback in real-time.

Running Small Language Models on Edge Devices

SLMs bring AI power directly to your smartphone (using PockPal) or PC (using Ollama), offering offline access, enhanced privacy, and lower latency.

SLMs on Mobile Device with PocketPal

For users interested in experiencing SLMs firsthand, the PocketPal AI app offers an intuitive way to interact with these models directly on your smartphone, without the need for an internet connection. Whether you want to draft emails, brainstorm ideas, or get answers to quick questions, PocketPal provides a seamless interface powered by optimized SLMs. Its offline capabilities ensure your queries remain private.

Features

• Offline AI Assistance: Run language models directly on your device without internet connectivity.
• Model Flexibility: Download and swap between multiple SLMs, including Danube 2 and 3, Phi, Gemma 2, and Qwen.
• Auto Offload/Load: Automatically manage memory by offloading models when the app is in the background.
• Inference Settings: Customize model parameters like system prompt, temperature, BOS token, and chat templates.
• Real-Time Performance Metrics: View tokens per second and milliseconds per token during AI response generation.

Download PocketPal AI on iOS and Android

Running SLMs on PC with Ollama

Ollama, an open-source tool, simplifies SLM deployment on PCs:

• Local Management: Run models like Llama3.2-1B or Phi-3.5 Mini with minimal setup.
• GPU Optimization: Leverages consumer-grade GPUs for faster inference.
• Custom Workflows: Integrate SLMs into data pipelines or creative tools (e.g., automated code reviews).

Getting Started with Ollama:

1. Install Ollama from ollama.com

2. Open the terminal and download a model:

ollama pull gemma2:2b

3. Run the model interactively:

ollama run gemma2:2b

This setup enables local AI-powered chatbots, coding assistants, and document summarization without needing cloud services.

Fine-Tuning Small Language Models

One of the most exciting aspects of SLMs is their adaptability through fine-tuning. By exposing an SLM to domain-specific datasets, you can enhance its performance for niche applications.

For instance:

• Fine-tune a model on legal documents to create a contract analysis assistant.
• Train an SLM on technical manuals to build a troubleshooting guide for engineers.

There are several ways to fine-tune an SLM:

1. Full Fine-Tuning – Retraining all parameters with new data (requires significant compute).
2. LoRA (Low-Rank Adaptation) – Fine-tunes only a few layers, making it lightweight and efficient.
3. Adapters & Prompt Tuning – Adds extra layers or optimizes prompts to guide model responses.

Example: Fine-Tuning with LoRA Using Hugging Face’s peft library:

from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gemma-2-2b"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

config = LoraConfig(r=8, lora_alpha=16, lora_dropout=0.1)
model = get_peft_model(model, config)

# Train the model on new data...

Fine-tuning not only improves accuracy but also ensures the model aligns closely with your unique requirements.

Conclusion

Small Language Models (SLMs) represent a crucial step toward efficient, accessible, and cost-effective AI. They provide practical solutions for businesses, developers, and researchers looking for powerful AI without the heavy computational burden of LLMs.

With tools like Ollama for PCs and fine-tuning options for customization, SLMs are reshaping the AI landscape—making AI more personal, private, and available to everyone.

Let's discover how compact AI can transform our projects.