Comments (2)
Yeah, sure. Isn't MT5 model just the T5 model, but trained on multilingual dataset?
Anyways, to use any different model for the T5 pre-training you need to make sure that your forward function has compatible signature to that of the vanilla T5 model.
Furthermore you need to import and include your model in this model dictionary.
The model config from Huggingface is imported here, but all you need to do is to just change args.model.name
to your desired model.
from nanot5.
Hope I helped, feel free to ask if you have more questions, happy to help
from nanot5.
Related Issues (20)
- RMS scaling issues HOT 15
- pre-train on long context. HOT 1
- How to run on CPU HOT 1
- 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
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