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You are here		blog.moonglow.ai Three Kuhnian Revolutions in ML Training
\|	\|	lunary.ai Anthropic Tokenizer \| Lunary	2.9 parsecs away Travel
\|	\|	Use this tool below to understand how a piece of text might be tokenized by Anthropic models (Claude 1, Claude 2, Claude Instant, Claude 3) and the total count of tokens in that piece of text.	2.9 parsecs away Travel
\|	\|	www.alignmentforum.org New Scaling Laws for Large Language Models - AI Alignment Forum	1.9 parsecs away Travel
\|	\|	On March 29th, DeepMind published a paper, "Training Compute-Optimal Large Language Models", that shows that essentially everyone -- OpenAI, DeepMind...	1.9 parsecs away Travel
\|	\|	deepmind.google An empirical analysis of compute-optimal large language model training - Google DeepMind	0.4 parsecs away Travel
\|	\|	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...	0.4 parsecs away Travel
\|	\|	blog.pamelafox.org Using SLMs in GitHub Codespaces	24.7 parsecs away Travel
\|		Today I went on a quest to figure out the best way to use SLMs (small language models) like Phi-3 in a GitHub Codespace, so that I can...	24.7 parsecs away Travel