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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
➡️New build available for Xiaomi POCO M3/Redmi 9T (chime)
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ℹ️ Version: 12
📆 Build date: November 25, 2022 01:10
📂 File size: 1.66 GB
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➡️New build available for Xiaomi POCO M3/Redmi 9T (chime)
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ℹ️ Version: 12
📆 Build date: November 20, 2022 11:41
📂 File size: 1.66 GB
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#chime#pixelexperience
🪐 One of the largest samples of fast radio bursts (FRBs) ever recorded comes from the Canadian Hydrogen Intensity Mapping Experiment (CHIME), which has detected thousands of these brief, mysterious flashes from all directions across the sky. Each FRB is a burst of radio energy that lasts just milliseconds, often releasing more power than our Sun emits in days, and their unpredictable appearances continue to challenge scientists searching for their true origins. ✨
#FRB⚡#CHIME⚡#astronomy⚡#nasa⚡#galaxy⚡#stars⚡#universe⚡#cosmos⚡#space
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