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Andrej Karpathy's explaination of Bigram Language Model explained Bigram LMs, though simple, it provides powerful insight into the inner mechanics of how tokens are processed in language models. This is a pre-amble for what's next: GPT, which is the 2nd part of the series The Bigram model here incorporates: Tokenization, Vocabulary, Negative Loss Function, Cross Entropy, Logits, SoftMax, Optimizer, and AdamW These are essential ingredients to understand in order to build our knowledge on how LLM
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I tried this VIRAL exercise by @nell.grabowski and I can say I should be served up some humble pie after attempting this one! If you have been wanting to take your body to the next level and challenge yourself to the ultimate test, then look no further and give this a go! Try it and let me know whether you gave it a pump 💪🏻 or dump 👎🏻 #abs #gym #gymmotivation #functionaltraining #abworkout
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Want to stop AI hallucinations?. AI grounding can do it for you. Instead of relying on frozen training data, grounded AI pulls real-time facts from your documents, databases, or live search. Fewer hallucinations. Verifiable answers. Actually useful AI. 🧠✅ Learn what's changing AI accuracy in 2026 with the link in bio! #AI #Grounding #MachineLearning #LLM #Tech
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TikToklorka.ai
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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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