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transformers/docs/source/en/model_doc/flan-t5.md

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+*This model was released on 2022-10-20 and added to Hugging Face Transformers on 2023-06-20.*
+
+# FLAN-T5
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## 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")
+>>> 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-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>