The rise of small language models (SLMs)
Following the popularity of LLMs, we have seen a rise in SLMs. Researchers began exploring SLMs as a response to the challenges posed by their larger counterparts. While large models offer impressive performance, they also bring substantial demands in terms of computational resources, energy consumption, and data requirements. These factors limit accessibility and practicality, especially for individuals and organizations with constrained resources.
The architecture of SLMs is fundamentally similar to that of LLMs, with both based on the transformer architecture (for example, Llama). The differences mainly lie in the scale and some specific optimizations tailored to their respective use cases. Language models in the range of millions and the order of 10 billion parameters or less are considered to be SLMs. They are streamlined versions of language models that are designed to deliver a balance between performance and efficiency. Unlike their larger counterparts, SLMs require significantly less computational power and data to train and run, making them more accessible, lower cost to build, and environmentally friendly.
Examples of SLMs include Tiny Llama (1.1 B parameters), Llama 2 (7 B parameters), Orca-2 (7B, 13B parameters) and Phi-2 (2.7B parameters), Mistral (7B parameters), and Falcon-7B, and each offers a unique trade-off between size, speed, and performance.
Phi-2, an open source model developed by Microsoft, trained intextbook quality data, sets a new standard in performance efficiency, outshining models tenfold its size across a range of popular benchmarks. This model showcases greater proficiency in areas such as commonsense reasoning, language understanding, mathematical problem-solving, and coding!
Let’s look at the benefits of SLMs:
- Efficiency: SLMs, with their fewer parameters, offer notable computational advantages over larger models such as GPT-3. They provide quicker inference speeds, demand less memory and storage, and use smaller datasets for training compared to larger models.
- Fine-tunable: SLMs can be easily tailored to specific domains and specialized uses.
- Easy access: Since they are often open source, they democratize access to advanced NLP capabilities, allowing a broader range of users and developers to incorporate sophisticated language understanding into their applications.
- Deployment on the edge: Additionally, the reduced resource requirements of SLMs make them ideal for deployment in edge computing scenarios – offline mode and on devices with limited processing capabilities.
Moreover, their lower energy consumption contributes to a more sustainable AI ecosystem, addressing some of the environmental concerns associated with larger models.
While SLMs are gaining traction, some are not yet fully developed for production use. However, we expect continued enhancements in their efficiency and readiness for deployment. Furthermore, SLMs are set to become a core component in edge devices such as smartphones and other cutting-edge gadgets. This trend presents an exciting segue into the next section, where we’ll delve into the opportunities this technology brings to edge devices.
