Google's T5-v1.1-base pre-trained for 24 hours (80k steps / 256 batch size) on a single GPU in nanoT5 library for efficient pre-training.
For more details about the model refer to the original paper and original model weights.
It can be further fine-tuned on SuperNatural-Instructions dataset to achieve comparable performance to the same model pre-trained on 150x more data through "a combination of model and data parallelism [...] on slices of Cloud TPU Pods", each with 1024 TPUs.
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