Creating a dataset for LLM to prevent overfitting. #DataPreparation

The article discusses the process of creating a dataset for fine-tuning a Large Language Model (LLM) for memorization without generalization. The author explains how to extract text from a video transcription, reformat it into paragraphs, and generate a set of Q&A pairs using a script and the langchain library. The author also provides code snippets for these processes and includes screenshots of the formatted text. The article emphasizes the importance of editing the Q&A pairs for clarity and mentions the need to train the model with a dataset that includes both training and validation data. The author highlights that experimenting with LLMs can be challenging but not impossible, and by following the outlined steps, users can effectively customize LLMs for their specific needs. Overall, the article serves as a guide for creating a dataset for fine-tuning LLMs and demonstrates the practical application of this process.

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Source link: https://medium.com/@meirgotroot/training-for-overfitting-26b8c93037dd?source=rss——ai-5