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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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Your eval score went from 89% to 92%. That's not an improvement. ⚡ At 100 test cases, your margin of error is roughly ±8 points. A 3-point win is noise. Most teams ship on numbers that can't survive a stats check. 12 AI evaluation concepts — the method, not the tooling 👇 🟢 THE SET 1. Golden set — expected behaviour, written down and versioned. 2. Slices — 92% overall, 61% on long context. The average is where failures hide. 3. Sample size — n=100 means ±8 points. You need ~1,000 cases to detec
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