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+*This model was contributed to Hugging Face Transformers on 2023-06-20.*
+
+# FLAN-UL2
+
+
+## Overview
+
+[Flan-UL2](https://www.yitay.net/blog/flan-ul2-20b) 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. Similar to `Flan-T5`,  one can directly use FLAN-UL2 weights without finetuning the model:
+
+According to 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:
+
+The original checkpoints can be found [here](https://github.com/google-research/google-research/tree/master/ul2).
+
+## Running on low resource 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, BitsAndBytesConfig
+
+
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-ul2", quantization_config=BitsAndBytesConfig(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").to(model.device)
+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']
+```
+
+<Tip>
+
+Refer to [T5's documentation page](t5) for API reference, tips, code examples and notebooks.
+
+</Tip>