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*This model was published in HF papers on 2022-10-20 and contributed to Hugging Face Transformers on 2023-06-20.*

# FLAN-T5


## Overview

FLAN-T5 was released in the paper [Scaling Instruction-Finetuned Language Models](https://huggingface.co/papers/2210.11416) - 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", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")

inputs = tokenizer("A step by step recipe to make bolognese pasta:", return_tensors="pt").to(model.device)
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-large)

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

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

The original checkpoints can be found [here](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints).

<Tip>

Refer to [T5's documentation page](t5) for all API reference, code examples and notebooks. For more details regarding training and evaluation of the FLAN-T5, refer to the model card.

</Tip>
