Leveraging MapReduce and LLMs for Big Data Systems - A Potential Benefit for Your Project

[elricwan]

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1. Is this request related to a challenge you're experiencing? Tell me about your story.

Hi there,

I recently wrote an article discussing how to combine MapReduce with small-scale LLMs (Large Language Models) for large-scale text processing tasks. In the article, I detailed this innovative approach and demonstrated it with a practical Q&A system using text from the Harry Potter series. I proved that small LLMs like Gemma2 can achieve better performance than GPT4o and MapReduce can reduce the processing time.

Dify is the leading framework for Agent framework, I believe the concepts and methods discussed in the article might be of interest and benefit to you or other developers in the community. I’d love to share this with you and open up a discussion.

Article Link: Click here to view

Looking forward to your feedback and discussion! Thank you!

Best regards, elricwan

https://github.com/user-attachments/assets/cdb40a2c-7de5-42ba-8eb2-1ddbded72677

2. Additional context or comments

Traditionally, Apache Hadoop and Apache Spark frameworks have been paired with conventional machine learning models, they frequently fall short in more demanding tasks that require a deep semantic understanding. In contrast, small-scale LLMs have the ability to utilize contextual information to more accurately understand and manage these complex tasks, showing exceptional performance particularly in areas like text comprehension, content extraction, and automatic tagging. we can combine the intelligent reasoning of LLMs with the parallel processing strength of MapReduce. By doing so, we can resolve the tension between efficiency and performance in large-scale text processing.

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