As we’ve mentioned several times in this book, large
language models have had a big impact on the field of NLP, and current
trends suggest that this isn’t going to stop any time soon, as Figure A-1 suggests.
Figure A-1. Language model growth trend
The great thing about this, even if you’re not particularly
enthusiastic about training a large model yourself, is that most
researchers are generally interested in open sourcing their code and
releasing the trained model weights as well. Better language models
trained on larger datasets for longer means that you, the developer building NLP applications, has a stronger baseline to work off of. It’s almost like a free performance boost!1
Because of this rapid progress and general interest in open sourcing the
best models, we generally wouldn’t recommend training your
own large language model from scratch. It is often counterproductive
when many researchers have spents years of GPU time optimizing a
specific language model on an existing large dataset. Our very first
lesson in Chapter 2 was that being prudent with fine-tuning can
reap huge rewards. In practice, you always want to use transfer
learning wherever you can.
However, if you do have the luxury of being able to access large
amounts of compute, there are some things you should know about scaling
your model training to ensure optimal performance.
Multi-GPU ...
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