Chinchilla
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- LLM Pretraining Engineering: Frameworks, Parallelism, and Scaling Laws
The supplied article surveys large-model pretraining, covering data, tokenization, optimizers, memory, distributed parallelism, checkpoints, and model evaluation. It compares Megatron-LM, DeepSpeed, and torchtitan; explains the different configuration guidance in Kaplan’s and Chinchilla’s scaling laws; and discusses cost estimation, loss spikes, checkpoint recovery, and resuming distributed training. Framework and performance characterizations are the article’s assessments, while its cost calculation is presented without accompanying source calculations.
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