Small language models (SLMs) are compact versions of LLMs, with fewer parameters and simpler designs. Unlike LLMs, which can contain billions of parameters, SLMs focus on efficiency and accessibility. Here are some of their advantages:
- Model size and complexity: SLMs have fewer parameters. For example, while an LLM like ChatGPT (GPT-4) can contain 1.76 trillion parameters, an open-source SLM like Mistral 7B has only 7 billion parameters. Reduced model complexity makes SLMs easier to understand and debug.
- Resource efficiency: Training an LLM is resource-intensive, requiring large-scale cloud graphics processing unit (GPU) units. In contrast, SLMs can run on local machines and generate data in an acceptable time. This is especially useful in environments with limited resources.
- Lower bias: LLMs tend to have inherent biases due to the data they are trained on. SLMs, being smaller and trained in specific domains, can mitigate some of these biases.
- Quick implementation: SLMs require less data and training time. They can be ready in minutes to hours, compared to the time needed for LLMs. This facilitates their deployment on smaller devices or in local environments.
Rising trend:
SLMs are gaining popularity due to their practicality and versatility. They are ideal for specific applications such as chatbots, virtual assistants, and recommendation systems. Additionally, their smaller size and resource requirements make them more accessible to a wide range of users.
In summary, while LLMs offer advanced capabilities and excel in complex tasks, SLMs provide a more efficient and accessible solution. Xira specializes in dedicated Small language models for expertise in collections, sales, and customer service. Schedule an appointment to learn more.
