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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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Host Testing and evaluation

@HostEvaluate · Post #811 · 08/11/2022, 04:45 AM

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#SYM#TW Host Provider: SYM Specification: 1vCore | 1G RAM | 20G SSD | 20TB @ 300Mbps down; 100Mbps up | $ 18 / Mo (Promo code: 88-10-300M) 感谢商家提供的测试机。hinet vps, 月 50% SLA 保障。PayPal 和支付宝老用户审核才可使用。要实名. 更多折扣码 https://paste.red/p/0ead2349a779

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Host Testing and evaluation

@HostEvaluate · Post #746 · 01/25/2022, 03:45 PM

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#TW#SYM Host Provider: SYM Location: Kaohsiung, Taiwan Specification: 1vCore | 1G RAM | 30G Storage | Unlimited @ 100M up, 300M down | $15 / Month Test IP: Contact @wartw https://paste.ubuntu.com/p/xft3b5ypTm/ 硬盘应该是 HDD

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