Byte-Pair Encoding tokenization - Hugging Face LLM Course
We’re on a journey to advance and democratize artificial intelligence through open source and open science.| huggingface.co
We’re on a journey to advance and democratize artificial intelligence through open source and open science.| huggingface.co
Just over a year ago, I wrote about how integer tokenization using the GPT2 and GPT3 tokenizer was insane. This was because that it failed to create a coherent number representation in token space since large numbers of integers were assigned a single unique token, and even multi-token integers were...| www.beren.io
Musings of a Computer Scientist.| karpathy.github.io
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size ...| arXiv.org
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These re...| arXiv.org
