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  "title": "What is Temperature in LLMs?",
  "body_markdown": "Dec 16, 2024\nLLMs\nWhat is Temperature in LLMs?\nPredictable ↔ Random.\nAvi Chawla\n👉\n​\n​\nA low temperate value produces identical responses from the LLM (shown below):\nBut a high temperate value produces gibberish.\nWhat exactly is\ntemperature\nin LLMs?\nLet’s understand this today!\nTraditional classification models use softmax to generate the final prediction from logits over all classes. In LLMs, the output layer spans the entire vocabulary.\nThe difference is that a traditional classification model predicts the class with the highest softmax score, which makes it deterministic.\nBut LLMs\nsample\nthe prediction from these softmax probabilities:\nThus, even though “\nToken 1\n” has the highest probability of being selected (\n0.86\n), it may not be chosen as the next token since we are sampling.\nTemperature introduces the following tweak in the softmax function, which, in turn, influences the sampling process:\n1) If the temperature is low, the probabilities look more like a max value instead of a “soft-max” value.\nThis means the sampling process will almost certainly choose the token with the highest probability.\nThis makes the generation process look greedy and (almost) deterministic.\n2) If the temperature is high, the probabilities start to look like a uniform distribution:\nThis means the sampling process may select any token.\nThis makes the generation process random and heavily stochastic.\nA quick note: In practice, the model can generate different outputs even if\ntemperature=0\n. This is because there are still several other sources of randomness, such as race conditions in multithreaded code.\nHere are some best practices for using temperature:\nSet a low temperature value to generate predictable responses.\nSet a high temperature value to generate more random and creative responses.\nAn extremely high temperature value rarely has any real utility, as we saw at the top.\nAnd this explains the objective behind temperature in LLMs.\n​\n👉 Over to you: How do you determine an ideal value of temperature?\nThanks for reading!\nPublished on Dec 16, 2024\nComments\nShare\nCopy link\nShare to X\nShare to Facebook\nShare to Linkedin\nCopied\nPrevious\nA Crash Course on Building RAG Systems – Part 6 (With Implementation)\nNext\nA Crash Course on Building RAG Systems – Part 7 (With Implementation)",
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