How giant language models got easier to train — and why it matters
Imagine training a brain-sized computer program with more than 8 billion pieces and not running out of memory.
A team found a clever way to split those pieces so the model can be trained across many machines, without changing deep software or making things messy.
That means big models can be built faster, with less fuss, and scaled up more easily.
The result is faster training and models that learn better language, so apps can understand and write more naturally.
They proved it by training very large models and getting state of the art results on tricky language tests, beating older top scores.
It’s a game-changing step for anyone who wants smarter chatbots, better search, or tools that help people write.
This does not fix every problem, but it opens the door to more powerful tools that are easier to make, and that could touch lots of everyday apps, sooner than expected, which is exciting.
Read article comprehensive review in Paperium.net:
Megatron-LM: Training Multi-Billion Parameter Language Models Using ModelParallelism
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