🚀RAG Implementation for HotPotQA ColBERT Qwen 25 72B FREE INSTANCE

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⚡ In this tutorial, we’ll explore the fascinating world of Retrieval-Augmented Generation (RAG) systems and how to implement them in under 100 lines of code! We’ll cover everything from understanding RAGs, integrating them into existing pipelines, to using Qwen 2.5 72B for your generative tasks. I’ll also walk you through implementing a simplified RAG pipeline using DSPy, CoBERTv2 for retrieval, and the HotPotQA dataset for multi-hop question answering. Whether you're new to RAGs or want to streamline your workflow, this video is packed with insights and practical steps. Stay tuned for more! • 📚 DSPy Documentation: https://dspy-docs.vercel.app/ • 🔮 Tune Studio: https://studio.tune.app/playground • 🧑‍💻 Dev.to Article: https://dev.to/aryankargwal/doing-mul... • 🔗 GitHub Repo: https://github.com/aryankargwal/nlp-t... • 🔔 Subscribe for weekly LLM tutorials! • 🎥 Chapters • 0:01 Introduction • 0:35 What are RAGs? • 1:35 Tutorial • 1:46 Dataset • 2:40 Retrieval Model • 3:28 Generative Model • 4:23 RAG Implementation • 5:06 Final Output

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