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 simonwillison.net
Could you train a ChatGPT-beating model for $85,000 and run it in a browser?
| | cset.georgetown.edu
The Surprising Power of Next Word Prediction: Large Language Models Explained, Part 1 | Center for Security and Emerging Tech...
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| | Place to find CSET's publications, reports, and people
| | deepmind.google
An empirical analysis of compute-optimal large language model training - Google DeepMind
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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...
| | 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
| | blog.vstelt.dev
Writing a Neural Network from Scratch in Rust
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| [AI summary] The article explains the process of building a neural network from scratch in Rust, covering forward and backward propagation, matrix operations, and code implementation.