FLAN-T5
Overview
FLAN-T5 was released in the paper Scaling Instruction-Finetuned Language Models - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.
One can directly use FLAN-T5 weights without finetuning the model:
thon

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
inputs = tokenizer("A step by step recipe to make bolognese pasta:", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['Pour a cup of bolognese into a large bowl and add the pasta']

FLAN-T5 includes the same improvements as T5 version 1.1 (see here for the full details of the model's improvements.)
Google has released the following variants:

google/flan-t5-small

google/flan-t5-base

google/flan-t5-large

google/flan-t5-xl

google/flan-t5-xxl.

The original checkpoints can be found here.

Refer to T5's documentation page for all API reference, code examples and notebooks. For more details regarding training and evaluation of the FLAN-T5, refer to the model card.