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# FLAN-T5

## Overview

FLAN-T5 was released in the paper [Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf) - 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:

```python
>>> 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](https://huggingface.co/docs/transformers/model_doc/t5v1.1) for the full details of the model's improvements.)

Google has released the following variants:

- [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)

- [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)

- [google/flan-t5-large](https://huggingface.co/google/flan-t5-base)

- [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl)

- [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl).

One can refer to [T5's documentation page](t5) for all tips, code examples and notebooks. As well as the FLAN-T5 model card for more details regarding training and evaluation of the model.

The original checkpoints can be found [here](https://github.com/google-research/t5x/blob/main/docs/models.md#mixture-of-experts-moe-checkpoints).