Fine-tuning enables you to extract additional benefits from the models accessible via the API by offering:
Higher quality outcomes than those achieved through prompting.
Capacity to train on a greater number of examples than can be accommodated in a prompt.
Token savings as a result of shortened prompts.
Requests with reduced latency.
By training on a significantly greater number of examples than can be accommodated in the prompt, fine-tuning enhances few-shot learning, enabling the attainment of superior results on a diverse array of tasks. The number of examples required in the prompt will decrease once a model has been refined. This reduces expenses and facilitates requests with reduced latency.
In this video, we shall fine-tune gpt-4-o-mini with custom dataset
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