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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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pixiv

@pixiv · Post #308 · 09/26/2017, 02:10 PM

#Dragon ポ~ン(出水ぽすか) pixiv.net/i/64963600

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@PTPPAction · Post #2352 · 09/18/2024, 06:44 AM

#dragon#open 抢先体验版本不保证稳定性 feat:调整图表样式 commit 调整图表样式 * 调整图表样式 * Merge branch 'pt-plugins:dev' into dev * feat:调整图表样式 描述 * feat:调整图表样式 X轴日期格式保持一致

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@PTPPAction · Post #2325 · 09/11/2024, 08:04 AM

#dragon#open 抢先体验版本不保证稳定性 feat:调整图表样式 commit feat:调整图表样式 描述 * feat:调整图表样式 X轴日期格式保持一致

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@PTPPAction · Post #2354 · 09/18/2024, 01:16 PM

#dev#dragon#merged 推荐下载 feat:调整图表样式 (#1994) commit feat:调整图表样式 (#1994) * feat:调整图表样式

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