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 www.lesswrong.com
New Scaling Laws for Large Language Models - LessWrong
| | blog.moonglow.ai
Three Kuhnian Revolutions in ML Training
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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
| | www.alignmentforum.org
New Scaling Laws for Large Language Models - AI Alignment Forum
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| | On March 29th, DeepMind published a paper, "Training Compute-Optimal Large Language Models", that shows that essentially everyone -- OpenAI, DeepMind...
| | codeincomplete.com
AI, ML, and Large Language Models | Jake Gordon
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| | Personal Website for Jake Gordon
| | amatria.in
Transformer models: an introduction and catalog?-?2023Edition - AI, software, tech, and people. Not in that order. By X
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| [AI summary] The provided text is an extensive overview of various large language models (LLMs) and their architectures, training tasks, and applications. It includes detailed descriptions of models like GPT, T5, BERT, and others, along with their pre-training objectives, parameter counts, and specific use cases. The text also references key research papers, surveys, and resources for further reading on LLMs and related topics.