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Source channel @githubtrending · Post #14993 · Jul 24
#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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@The2ndDim · Post #914 · 03/27/2019, 07:04 PM
#CAUTION 如果最近有要退换货或者售后维修的话，建议在装箱之前，拍摄一段视频，说明故障原因，为何送修，设备当前的状况，以及拍下来能证明当时的日期时间的东西。最好视频本身直接一镜到底录下来一直快递员最终带走包裹的画面。 最近似乎有一些售后维修会故意损坏送修的设备然后拒绝提供维修/保修服务。 如果你遭遇了这个情况，录制的这个视频将可能为你的维权提供一些帮助。
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@venturevillagewall · Post #4229 · 02/24/2025, 10:00 AM
Crypto Outflows Reach $508M Amid Caution Digital asset investment products experienced significant outflows of $508 million as investors react cautiously to uncertainties following the US Presidential inauguration. Influencing factors include market volatility surrounding trade tariffs, inflation, and monetary policy adjustments. Read more: Unfolded #Crypto#Investment#USPolitics#MarketVolatility#Inflation#MonetaryPolicy#DigitalAssets#DeFi#Ethereum#Trading#Finance#Economics#Outflows#Blockchain#Investors#Caution#RiskManagement#CryptoNews#MarketTrends#AssetManagement