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2:00
Complete LLM Basics Guide || Large Language Model Explained (LLM) for beginners
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Karthik's Show
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Fable 5.1 y el supuesto marcado de agua en textos generados por IA 🤖💧 El concepto es interesante: algunos sistemas pueden introducir patrones en la forma en que el modelo selecciona tokens, haciendo que determinadas salidas sean identificables mediante herramientas de detección. En este caso, el proceso explicado es: 🧠 Generar el contenido con Claude 🔄 Reescribirlo completamente con otro modelo ⚙️ Integrar ese proceso como una skill 📄 Obtener una nueva versión del texto Pero ojo: no basta c
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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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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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