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Fine-Tuning GPTs on Azure with a User-Friendly Dashboard
Learn how to fine-tune a GPT model using Azure OpenAI Studio — UI Dashboard.
By Korkrid Kyle Akepanidtaworn, He Zhang
What?
Fine-Tuning, or Supervised Fine-Tuning, is a critical process in the development and optimization of large language models (LLMs). This technique involves retraining an existing pre-trained LLM using a curated set of example data. The primary objective is to adapt the model to perform specific tasks more effectively by leveraging task-specific examples. Specifically, the LLM is partially retrained using <input, output> pairs of representative examples of the desired behavior. Hence, it involves updating the model weights.
Fine-tuning is a common practice in Generative AI. It distinguishes itself from prompt engineering and retrieval-augmented generation, where LLMs acquire new skills permanently.
