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What are Small Language Models (SLMs)?

Small Language Models (SLMs) are AI models built to understand and generate human language. They’re referred to as “small” because they contain fewer parameters than Large Language Models (LLMs) like GPT-3. This makes them more lightweight, efficient, and suitable for applications with limited computing resources or memory. 

One of the main advantages of SLMs is their ability to run effectively on basic hardware, making them versatile for a wide range of environments. They’re ideal for situations where the full capabilities of a large model aren’t necessary. Additionally, SLMs can be easily fine-tuned for specific tasks, offering tailored performance. For businesses just beginning to explore Generative AI, SLMs provide a fast and straightforward setup. Unlike general-purpose LLMs, SLM AI models can be fine-tuned quickly for industry-specific applications.

What is a Large Language Model (LLM)?

A Large Language Model (LLM) is an advanced AI model designed to understand user input and respond in a way that resembles human communication. These models are developed using deep learning techniques, which enable them to process and generate text that closely mimics natural language. 

LLMs are built on a transformer architecture, a highly complex framework that performs exceptionally well when handling large volumes of data. 

This architecture includes two main components: the encoder and the decoder. When data is fed into the model, it is divided into smaller units called tokens. These tokens are then processed through a series of mathematical operations that uncover intricate relationships between them. This allows the model to recognize patterns and context, helping it provide more accurate and human-like responses to similar queries in the future.