| FLAN-T5 | |
| Overview | |
| FLAN-T5 was released in the paper Scaling Instruction-Finetuned Language Models - it is an enhanced version of T5 that has been finetuned in a mixture of tasks. | |
| One can directly use FLAN-T5 weights without finetuning the model: | |
| thon | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small") | |
| tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small") | |
| 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)) | |
| ['Pour a cup of bolognese into a large bowl and add the pasta'] | |
| FLAN-T5 includes the same improvements as T5 version 1.1 (see here for the full details of the model's improvements.) | |
| Google has released the following variants: | |
| google/flan-t5-small | |
| google/flan-t5-base | |
| google/flan-t5-large | |
| google/flan-t5-xl | |
| google/flan-t5-xxl. | |
| The original checkpoints can be found here. | |
| Refer to T5's documentation page for all API reference, code examples and notebooks. For more details regarding training and evaluation of the FLAN-T5, refer to the model card. | |