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Qué es la atención en un LLM: la analogía de la biblioteca Query es lo que buscás, Key es el lomo del libro, Value es el contenido. Así funciona la atención sin fórmulas. #IA #LLM #shorts
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LLM just made running 70 billion parameter AI models possible without supercomputers by loading layers from your hard drive. Its flash attention keeps memory low, letting complex models like LLAMA 3.3 70B run smoothly on MacBooks and gaming PCs. A game-changer for students and indie devs exploring AI. #llm #llama33b #aiinnovation #opensourceai #machinelearning
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What happens to AI voice calling startups when the LLM companies keep releasing new models and eventually reduce the power, support, or availability of their previous models? This creates a serious problem for AI voice startups. Imagine building your entire voice-calling product around one model, signing enterprise customers, and then the underlying AI company launches a much more advanced model and starts phasing out the old one. Now you have to deal with: → Existing enterprise contracts → Voic
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Nobody ever wrote the code that lets ChatGPT write Python. Not one line of it. 🤯 Here's how a large language model actually gets built, step by step: 1️⃣ Data. Trillions of words of books, web pages, code and forums, then cleaned: duplicates removed, spam filtered, broken text stripped. One open dataset, FineWeb, is 15 trillion tokens of web text. 2️⃣ Tokens. The model never sees words.
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