kindly adding some documentations on t5-v1_1-base""
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Documentation: @sgugger
Hi
Could you kindly add some documentations on "t5-v1_1-base"? I tested one code with t5-base and t5-v1 version, for t5-v1 I got memory issue, this seems to me the model size is different and larger, also fast tokenizer for this model does not work, could you kindly add a documentation on these differences?

thanks a lot.



There is no model named `"t5-v1_1-base"` so I'm not sure what you mean.<|||||>Yes there is, `google/t5-v1_1-base`. Normally, t5_v1_1 and regular t5 aren't that different. From its [model card](https://huggingface.co/google/t5-v1_1-base):

> Version 1.1
T5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
Pre-trained on C4 only without mixing in the downstream tasks.
no parameter sharing between embedding and classifier layer
"xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger d_model and smaller num_heads and d_ff.
Note: T5 Version 1.1 was only pre-trained on C4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: C4

So for the base-sized model, normally the memory requirements are the same as t5-base. <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.<|||||>I have added documentation, see #13240. Therefore, closing.