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Supervised Fine Tuning for Large Language Models Explained

/ 1 min read

🧠✨ Supervised Fine Tuning Enhances Large Language Models for Domain-Specific Applications. The article discusses the process of Supervised Fine Tuning (SFT) for large language models (LLMs), enabling them to adapt to specific knowledge domains. It outlines the importance of data preparation, emphasizing the need for high-quality, relevant training data in a question/answer format. Techniques like Parameter Efficient Fine Tuning (PEFT) and LoRA are highlighted as resource-efficient methods for fine-tuning LLMs, even in limited GPU environments. The author shares personal experiences and challenges faced during the SFT process, including the use of another LLM for data preparation. Ultimately, the article serves as a guide for data scientists looking to customize LLMs for specialized tasks, with resources for further learning.

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