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Source channel @githubtrending · Post #14993 · Jul 24

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#jupyter_notebook Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability. https://github.com/langchain-ai/rag-from-scratch

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@PTPPAction · Post #2058 · 06/02/2024, 02:55 AM

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#hui-shao:dev #hui-shao #open 抢先体验版本不保证稳定性 fix(pig): HnR count. fix(pig): HnR count. 修复 H&R 计数 测试截图： !2024-06-02_104700

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@PTPPAction · Post #1475 · 12/25/2023, 03:15 PM

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#hui-shao:dev #hui-shao #open fix(u2): 修复非简体中文语言环境下上传量和下载量获取的问题

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@bubbletealuv · Post #2572 · 10/09/2024, 06:37 AM

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ᝬ 🤩𖠵 ゛ (( 🐰) new post by bubbletealuv ↝ #Hui ➛ #Pentagon.. ✨ …( emoji ) 💭 взяли? буду рада увидеть плюсы и реакции!!

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