Small language models (SLMs) are compact AI systems designed for efficient
processing of text data, suitable for various applications such as chatbots, text
generation, and language understanding in constrained environments. They are
distinguished from large language models (LLMs) by their smaller size, less complex
architecture, and lower computational requirements.
Key differences between SLMs and LLMs:
Criteria
Large Language Models (LLMs)
Small Language Models (SLMs)
Expansive architectures with billions of
parameters
Streamlined architectures with fewer
parameters
Complexity
Intricate and deep neural networks
More straightforward architecture,
less intricate
Training
requirements
Massive, diverse datasets for
comprehensive understanding
Limited datasets, tailored for specific
tasks
Computational
requirements
Significant resources, advanced
hardware required
Tailored for low-resource settings,
suitable for standard hardware
Applications
Ideal for advanced NLP tasks, creative
text generation
Suited for mobile apps, IoT devices,
resource-limited settings
Accessibility
Less accessible due to resource demands
and specialized hardware/cloud
computing
More accessible, deployable on
standard hardware and devices
Size
To leverage small language models effectively, fine-tuning is crucial to align their
capabilities with specific business needs or niche tasks. Fine-tuning smaller models
can be considerably less expensive regarding computational resources and data
volume required.
Examples of small language models include:
OpenELM Model by Apple: Efficient language model family designed for efficiency
and scalability, varying from 270 million to 3 billion parameters.
Microsoft's Phi-3: Smallest model in the family is Phi-3-mini with 3.8 billion
parameters, achieving performance comparable to larger models.
Experimenting with small language models for hyper-specific tasks that can run
locally is an active area of interest. Researchers believe there is room for many small
models trained to perform well-defined tasks, such as code adjustments from one
framework version to another