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Author: younesbelkada

docs/source/en/model_doc/flan-t5.mdx

@@ -16,12 +16,18 @@ specific language governing permissions and limitations under the License.
 
 FLAN-T5 was released in the paper [Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf) - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.
 
-One can directly plug in the weights of FLAN-T5 into a T5 model, like so:
+One can directly use FLAN-T5 weights without finetuning the model:
 
 ```python
->>> from transformers import T5ForConditionalGeneration
+>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
->>> model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
+>>> 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](https://huggingface.co/docs/transformers/model_doc/t5v1.1) for the full details of the model's improvements.)
@@ -38,6 +44,6 @@ Google has released the following variants:
 
 - [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl).
 
-One can refer to [T5's documentation page](t5) for all tips, code examples and notebooks.
+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#mixture-of-experts-moe-checkpoints).