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5 Open Source Small Language Models: A Guide with examples and use cases

Paras Madan

Paras Madan

— 4 min read
5 Open Source Small Language Models: A Guide with examples and use cases

Small Language Models, often known as SLMs, have become increasingly popular as a result of their effectiveness and accessibility.

Opposite to their larger counterparts, SLMs are intended to carry out particular tasks with a limited amount of computational resources.

This makes them an excellent choice for a wide range of applications, including chatbots and real-time translation respectively.

In this article, we will discuss five open-source SLMs (in increasing order of model size), outlining the distinctive characteristics of each of them in order to assist you in determining which one is most suitable for your LLM Pipeline using this information.

1. Qwen 2

Qwen 2 is a compact language model with both 0.5 billion parameters and 1.5 billion parameters, designed for efficiency and versatility.

The architecture of these models enables them to handle tasks such as text generation, summarization, and translation effectively.

Despite its smaller size, Qwen2-0.5B demonstrates competitive performance in benchmarks like MMLU and HumanEval, making it suitable for applications where computational resources are limited but robust language understanding is required.

2. TinyLlama

TinyLlama is a compact language model with 1.1 billion parameters, designed for efficiency and versatility.

It shares the same architecture and tokenizer as Llama 2, ensuring compatibility with existing Llama-based applications.

Pretrained on approximately 3 trillion tokens, TinyLlama excels in tasks such as text generation, summarization, and translation.

Its small size makes it ideal for deployment in environments with limited computational resources, while still delivering robust language understanding and generation capabilities.

3. Gemma-2

Gemma-2 is a 2-billion-parameter SLM that focuses on delivering high performance in a compact form.

It is designed to handle various NLP tasks efficiently, making it suitable for applications where computational resources are limited.

Gemma-2's open-source nature allows developers to adapt and integrate it into their specific use cases.

4. Phi-2

Phi-2 is a 2.7 billion-parameter language model developed by Microsoft, designed to deliver high performance in a compact form.

Utilizing a transformer-based architecture, it focuses on next-word prediction and has been trained on 1.4 trillion tokens from a mixture of synthetic and filtered web datasets.

Phi-2 excels in common sense reasoning, language understanding, mathematics, and coding, often outperforming larger models with up to 25 times more parameters.

5. StableLM Zephyr 3B

StableLM Zephyr 3B is a compact language model developed by Stability AI, featuring 3 billion parameters—making it 60% smaller than typical 7B models.

Despite its reduced size, it efficiently handles a wide range of text generation tasks, from simple queries to complex instructional contexts, without the need for high-end hardware.

The model is fine-tuned for instruction-following and question-answering tasks, making it suitable for applications like copywriting, summarization, instructional design, and content personalization.

Why use SLMs over LLMs

Small Language Models (SLMs) offer several advantages over Large Language Models (LLMs):

  1. Resource Efficiency: SLMs require less computational power, making them suitable for deployment on devices with limited resources, such as smartphones and IoT devices.
  2. Faster Inference: Due to their smaller size, SLMs provide quicker responses, which is essential for real-time applications like voice assistants and chatbots.
  3. Cost-Effective Deployment: SLMs are more affordable to train and maintain, making them accessible for businesses with limited budgets.
  4. Task-Specific Adaptability: SLMs can be fine-tuned efficiently for specialized tasks, often achieving performance comparable to larger models in specific domains.
  5. Reduced Energy Consumption: Operating SLMs consumes less energy, contributing to a lower environmental impact compared to the extensive resources required for LLMs.

These benefits make SLMs a practical choice for many applications, especially where resources are constrained or specific task optimization is required.

Conclusion

Small Language Models (SLMs) have emerged as a compelling alternative to their larger counterparts.

As we’ve discussed in this blog post, SLMs offer a powerful combination of flexibility and innovation, making them essential tools in today’s enterprise tech landscape.

By fine-tuning SLMs with their own data, enterprises can create models that are experts in their particular needs and the strategic importance for modern enterprises is clear: developing small language model capabilities is not just an option but a necessity.

In conclusion, SLMs represent the best of what AI has to offer: innovation, efficiency, and inclusivity. They are a reminder that sometimes, smaller really is better.

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