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<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# FLAN-T5
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## Overview
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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.
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One can directly use FLAN-T5 weights without finetuning the model:
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```python
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>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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>>> model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
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>>> tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
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>>> inputs = tokenizer("A step by step recipe to make bolognese pasta:", return_tensors="pt")
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>>> outputs = model.generate(**inputs)
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>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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['Pour a cup of bolognese into a large bowl and add the pasta']
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```
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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.)
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Google has released the following variants:
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- [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)
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- [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)
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- [google/flan-t5-large](https://huggingface.co/google/flan-t5-base)
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- [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl)
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- [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl).
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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.
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The original checkpoints can be found [here](https://github.com/google-research/t5x/blob/main/docs/models.md#mixture-of-experts-moe-checkpoints). |