Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the 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:
One can refer to T5’s documentation page 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.
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!
>>> 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']
The inference protocol is exactly the same as any T5
model, please have a look at the T5’s documentation page for more details.