Introduction to Fine-Tuning
Learn about the importance of fine-tuning for LLMs.
Overview
Fine-tuning refers to the process of adjusting the parameters of a pretrained model on a specific dataset to enhance its performance on a specific task. In large language models, fine-tuning is generally used to transform a foundation model into a specialized one for a particular use case.
Fine-tuning large language models brings several key benefits. It sharpens the model’s skills for specific jobs, like answering medical questions or writing legal documents. Learning how to fine-tune models can help individuals adapt powerful large language models for their specific use cases.
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