Introduction
In this article, we will explore the significance and applications of small language models, examine the pros and cons of SLMs, and discuss the Phi-3 family along with its key features.
Small Language Models (SLM)
Small language models are designed to excel at simpler tasks, making them more accessible and user-friendly for organizations with limited resources. They can be easily fine-tuned to meet specific needs and are ideal for applications that need to run locally on a device, where extensive reasoning is unnecessary, and a quick response is required.
The Phi-3 family
Phi-3 models are the most capable and cost-effective small language models (SLMs) available. They outperform models of the same size and the next size up across various language, reasoning, coding, and math benchmarks, thanks to their high-quality training data.
The availability of Phi-3 models broadens the range of high-quality options for Azure customers, providing them with more practical choices for developing and building generative AI applications.
The Phi-3 family consists of four models, each instruction-tuned and developed in accordance with Microsoft’s responsible AI, safety, and security standards, ensuring they are ready to use off-the-shelf.
- Phi-3-vision is a 4.2B parameter multimodal model with language and vision capabilities.
- Phi-3-mini is a 3.8B parameter language model, available in two context lengths (128K and 4K).
- Phi-3-small is a 7B parameter language model, available in two context lengths (128K and 8K).
- Phi-3-medium is a 14B parameter language model, available in two context lengths (128K and 4K).
![PHI 3]()
Fig: Source Official Microsoft Website
Phi-3-small, with only 7B parameters, surpasses GPT-3.5T in various language, reasoning, coding, and math benchmarks. The Phi-3-medium, featuring 14B parameters, continues this trend and outperforms Gemini 1.0 Pro. Additionally, Phi-3-vision, with just 4.2B parameters, excels over larger models like Claude-3 Haiku and Gemini 1.0 Pro V in general visual reasoning, OCR, table, and chart understanding tasks.
Comparison between SLM vs LLM
Here are the major differences between these 2 language models.
| Features |
SLM |
LLM |
| Size |
Less than 15 Million parameters |
Hundreds of billions of parameters |
| Computation |
Use mobile device processors |
GPU processors |
| Performance |
Handle Simple tasks |
Complex tasks |
| Deployment |
Easy to deploy |
Needs substantial infrastructure |
| Training |
Trained in a week |
Training can take 2 months |
Advantages of SLM
SLMs are becoming essential in various enterprise settings, offering significant benefits in efficiency, customization, and security. Their potential is particularly evident in the following.
- Domain-Specific Applications: SLMs can be fine-tuned with specific data, making them perfect for producing outputs customized to business requirements, such as automating customer service or providing support in data engineering.
- Specialized Tasks: Models like Atlas demonstrate how SLMs can use limited examples to perform tasks effectively, highlighting their adaptability and precision.
- Enterprise Efficiency and Productivity: By being trained on particular datasets, SLMs provide customized solutions that enhance productivity, automate tasks, and drive innovation within organizations.
- Security and Safety: SLM models are trained using high-quality data and further enhanced with safety post-training measures. These include reinforcement learning from human feedback (RLHF), automated testing and evaluations across numerous harm categories, and manual red-teaming.
![SLM Models]()
Fig: Advantages of SLM
Summary
In conclusion, small language models mark a notable transformation in the field of AI. Their efficiency, accessibility, and capacity for customization render them invaluable for developers and researchers in diverse fields. As these models advance, they promise to empower both individuals and organizations, shaping a future where AI is not only potent but also inclusive and adaptable to a wide range of requirements.