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www.alignmentforum.org | ||
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jeffreyladish.com
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| | | | | In this post I explain what we can and can't learn about AI sentience from conversations with Large Language Models. | |
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blog.moonglow.ai
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| | | | | Parameters and data. These are the two ingredients of training ML models. The total amount of computation ("compute") you need to do to train a model is proportional to the number of parameters multiplied by the amount of data (measured in "tokens"). Four years ago, it was well-known that if | |
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deepmind.google
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| | | | | We ask the question: "What is the optimal model size and number of training tokens for a given compute budget?" To answer this question, we train models of various sizes and with various numbers... | |
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rakuforprediction.wordpress.com
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| | | This document shows how to doFunction Callingworkflows with Large Language Models (LLMs) of OpenAI. | ||
