> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/transformers/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/transformers/api/models/text-models/flan-ul2.md).

# FLAN-UL2

## FLAN-UL2

### Overview

Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the [UL2](https://huggingface.co/docs/transformers/model_doc/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](https://huggingface.co/docs/transformers/model_doc/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 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!

Copied

```
>>> 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](https://huggingface.co/docs/transformers/model_doc/t5) for more details.