A community makerspace laser-booking desk cannot host a large general model but can further train a smaller one on its safety-card cases. Why might the shop fine-tune an SLM?
Select an answer to reveal the explanation.
Short Explanation
A smaller specialist can learn the safety cards without the power bill of a giant general model. Fine-tuning an SLM boosts task performance with less computational overhead. It is a lighter specialized option, not a license to skip review.
Full Explanation
Fine-tuning a small language model can improve specialized task performance while avoiding much of the compute cost of fine-tuning a large LLM. That makes SLMs attractive when resources are limited but domain fit matters. The item stays at conceptual depth without GPU-cluster sizing.