Small language model

Small language models (SLMs) are artificial intelligence language models designed for human natural language processing including language and text generation. Unlike large language models (LLMs), small language models are much smaller in scale and scope.

Typically, an LLM's number of training parameters is in the hundreds of billions, with some models even exceeding a trillion parameters. The size of any LLM is vast because it contains a large amount of information, which allows it to generate better content. However, this requires enormous computational power, making it impossible for an individual to train a large language model using just a single computer and GPU.

Small language models, on the other hand, use far fewer parameters, typically ranging from a few million to a few billion. This make them more feasible to train and host in resource-constrained environments such as a single computer or even a mobile device.^[1][2][3][4]

See also

• Edge computing