[Flan-UL2] Add-flan-ul2 (#21929)

* add doc and readme

* add model docs

* update toctree and fix copies

* update

* update doc file

* fix

* add FLAN-UL2 to configuration mapping

* fixup

* Apply suggestions from code review

* more clarification

---------

Co-authored-by: younesbelakda <younesbelkada@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

docs/source/en/model_doc/flan-ul2.mdx

@@ -0,0 +1,52 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# FLAN-UL2
+
+## Overview
+
+Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the [UL2](ul2) model released earlier last year. 
+It was fine tuned using the "Flan" prompt tuning and dataset collection. Similiar to `Flan-T5`,  one can directly use FLAN-UL2 weights without finetuning the model:
+
+
+According ot the original blog here are the notable improvements:
+
+- The original UL2 model was only trained with receptive field of 512, which made it non-ideal for N-shot prompting where N is large.
+- The Flan-UL2 checkpoint uses a receptive field of 2048 which makes it more usable for few-shot in-context learning.
+- The original UL2 model also had mode switch tokens that was rather mandatory to get good performance. However, they were a little cumbersome as this requires often some changes during inference or finetuning. In this update/change, we continue training UL2 20B for an additional 100k steps (with small batch) to forget “mode tokens” before applying Flan instruction tuning. This Flan-UL2 checkpoint does not require mode tokens anymore.
+Google has released the following variants:
+
+
+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#flan-ul2-checkpoints).
+
+
+## Running on low ressources devices
+
+The model is pretty heavy (~40GB in half precision) so if you just want to run the model, make sure you load your model in 8bit, and use `device_map="auto"` to make sure  you don't have any OOM issue!
+
+```python
+>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-ul2", load_in_8bit=True, device_map="auto")
+>>> tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")
+
+>>> 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))
+['In a large skillet, brown the ground beef and onion over medium heat. Add the garlic']
+```
+
+## Inference
+
+The inference protocol is exaclty the same as any `T5` model, please have a look at the [T5's documentation page](t5) for more details.

docs/source/en/model_doc/t5.mdx

@@ -74,6 +74,14 @@ Based on the original T5 model, Google has released some follow-up works:
 - **byT5**: byT5 is a T5 model pre-trained on byte sequences rather than SentencePiece subword token sequences. Refer
   to the documentation of byT5 which can be found [here](byt5).
 
+- **UL2**: UL2 is a T5 like model pretrained on various denoising objectives
+
+- **Flan-T5**: Flan is a pretraining methods that is based on prompting. The Flan-T5 are T5 models trained on the Flan collection of 
+    datasets which include: `taskmaster2`, `djaym7/wiki_dialog`, `deepmind/code_contests`, `lambada`, `gsm8k`, `aqua_rat`, `esnli`, `quasc` and `qed`.
+
+- **FLan-UL2** : the UL2 model finetuned using the "Flan" prompt tuning and dataset collection.
+
+
 All checkpoints can be found on the [hub](https://huggingface.co/models?search=t5).
 
 This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/google-research/text-to-text-transfer-transformer).