Model Architecture
Small Language Models (SLMs) are characterized by their compact neural network architectures, which typically consist of fewer layers, attention heads, and parameters compared to Large Language Models (LLMs). This streamlined design allows SLMs to achieve high performance while being more efficient and easier to deploy. The most common model architectures used in SLMs are:

Transformer-based architectures
DistilBERT and TinyBERT are popular choices due to their compact design and good performance.
These models use a distilled version of the original BERT architecture, reducing the number of layers, attention heads, and parameters while maintaining high accuracy.
Lightweight custom architectures
Some SLMs use custom lightweight architectures specifically designed for efficiency and performance on targeted tasks.
These architectures may incorporate techniques like weight pruning, quantization, and knowledge distillation to further reduce model size without sacrificing accuracy.
Efficient attention mechanisms
SLMs often employ efficient attention mechanisms to reduce computational complexity and memory footprint.
Examples include Linformer, Performer, and Reformer, which introduce approximations to the standard attention mechanism to improve efficiency.
Modular and composable designs
Some SLMs adopt a modular and composable design, allowing for flexible configuration and task-specific optimization.
This approach enables the model to be tailored to specific domains or applications by selectively activating relevant modules.
Knowledge distillation and transfer learning
SLMs leverage techniques like knowledge distillation and transfer learning to acquire knowledge from larger models and adapt it to their specific architectures.
This allows SLMs to benefit from the rich knowledge captured by LLMs while maintaining their own compact design and efficiency.
By carefully designing their neural network architectures and leveraging advanced techniques like knowledge distillation and efficient attention mechanisms, Small Language Models are able to achieve high performance while being more efficient, cost-effective, and easier to deploy compared to their larger counterparts. 1, 2, 3
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