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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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Venture Village Wall 🦄

@venturevillagewall · Post #4286 · 03/02/2025, 01:00 PM

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CZ Proposes New Token Unlock Model 🔵 CZ proposes a tokenomics model linking unlocks to price growth and time, reducing market impact. 😏 Initial 10% sold for development. 😏 Unlock every 6 months, contingent on 2x price increase maintained for 30 days. 😏 Max 5% unlock each time. ❓ Market implications include lack of sell pressure from mass unlocks and encouraging long-term projects. 🔴 Critics argue it could disadvantage small teams reliant on token price. Could this model reshape token economies? Read more: Link #CZ#Tokenomics#Crypto#Blockchain#Ethereum#MarketTrends#Investing#DeFi#Finance#Startups#Innovation#LongTerm#SmartContracts#PriceGrowth#Unlocks#Community#PricingModel#InvestmentStrategies#ElonMusk#HongKong