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You are here		deepmind.google An empirical analysis of compute-optimal large language model training - Google DeepMind
\|	\|	blog.moonglow.ai Three Kuhnian Revolutions in ML Training	0.4 parsecs away Travel
\|	\|	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	0.4 parsecs away Travel
\|	\|	www.alignmentforum.org New Scaling Laws for Large Language Models - AI Alignment Forum	0.9 parsecs away Travel
\|	\|	On March 29th, DeepMind published a paper, "Training Compute-Optimal Large Language Models", that shows that essentially everyone -- OpenAI, DeepMind...	0.9 parsecs away Travel
\|	\|	www.lesswrong.com New Scaling Laws for Large Language Models - LessWrong	1.2 parsecs away Travel
\|	\|	On March 29th, DeepMind published a paper, "Training Compute-Optimal Large Language Models", that shows that essentially everyone -- OpenAI, DeepMind...	1.2 parsecs away Travel
\|	\|	www.jeremymorgan.com How to Get Started With PyTorch	15.5 parsecs away Travel
\|		Want to learn about PyTorch? Of course you do. This tutorial covers PyTorch basics, creating a simple neural network, and applying it to classify handwritten digits.	15.5 parsecs away Travel