From the course: Generative AI: Working with Large Language Models

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Chinchilla

Chinchilla

From the course: Generative AI: Working with Large Language Models

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Chinchilla

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- [Instructor] Up to this point, we've seen that the trend has been to increase the model size. Interestingly, the number of training tokens used for most of these models has been around 300 billion. Now, the DeepMind team's hypothesis was that Gopher was too large. If you take the same compute budget, a smaller model trained on more data will perform better. They then tested this hypothesis by training over 400 language models, ranging from 70 million to over 16 billion parameters with data sets from five to 500 billion tokens. They then trained Chinchilla a 70 billion parameter model with 1.4 trillion training tokens and Chinchilla outperforms Gopher which has 280 billion parameters GPT-3 with its 175 billion parameters and Megatron-Turing NLG with its 530 billion parameters on a large range of downstream evaluation tasks. As this is a smaller model this means less computes required for fine tuning and inference.…

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