First came large language models (LLMs) that led to the formation of ChatGPT, built on GPT-3.5 and trained on 175B parameters. Though effective, LLMs are expensive to train, run and can be challenging to customise for specific tasks. Enter small language models (SLMs), which are more efficient to train, deploy and also more accurate. Additionally, they can also run on local infrastructure without resorting to GPU-rich third parties.
Realising the potential of SLMs, enterprises are rushing to develop new small language models.
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