Comments (1)
Excuse me the delay, you should do device=cpu
.
Also look out for other hyperparameters to set in the config file
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Related Issues (20)
- Difficulty applying NanoT5 to different model and database HOT 2
- AttributeError: Can't pickle local object 'IterableDataset.map.<locals>.<lambda>' HOT 1
- About pre-training on another dataset HOT 7
- Pre-training fails at step 30155 out of 32768 steps every time HOT 7
- self-defined loss function failed to work (torch._dynamo.exc.InternalTorchDynamoError: ln_encoder) HOT 4
- nanoT5 initializes lm_head weights with 768x too much variance, probably HOT 19
- Transformation to HF model
- Pre-train on different Dataset than C4 HOT 1
- Flash attention HOT 2
- Larger models and training on the Pile HOT 5
- How to create pytorch_model.bin file? HOT 1
- Silly question: Why do you need to re-implement T5 model? HOT 3
- Question about implementing whole word masking in nanoT5 HOT 1
- Beginner Question : Would it be wise to use this as a backbone for custom seq2seq modeling fMRI data and custom encoder? HOT 2
- Learning rate for multi-GPUs training HOT 3
- Just a quick question to pretrain Flan-T5 HOT 5
- Continued pretraining from official models. HOT 1
- pre-training on local C4 dataset? HOT 1
- About Pre-training objectives HOT 1
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