Update README.md

README.md

@@ -46,27 +46,6 @@ across four domains: documents, illustrations, user interfaces, and natural imag
 
 This model has been fine-tuned on VQA, you need to provide a question in a specific format, ideally in the format of a Choices question answering
 
-## Converting from T5x to huggingface
-
-You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_checkpoint_to_pytorch.py) script as follows:
-```bash
-python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --is_vqa
-```
-if you are converting a large model, run:
-```bash
-python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --use-large --is_vqa
-```
-Once saved, you can push your converted model with the following snippet:
-```python
-from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
-
-model = Pix2StructForConditionalGeneration.from_pretrained(PATH_TO_SAVE)
-processor = Pix2StructProcessor.from_pretrained(PATH_TO_SAVE)
-
-model.push_to_hub("USERNAME/MODEL_NAME")
-processor.push_to_hub("USERNAME/MODEL_NAME")
-```
-
 ## Running the model
 
 ### In full precision, on CPU:
@@ -140,6 +119,27 @@ print(processor.decode(predictions[0], skip_special_tokens=True))
 >>> ash cloud
 ```
 
+## Converting from T5x to huggingface
+
+You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_checkpoint_to_pytorch.py) script as follows:
+```bash
+python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --is_vqa
+```
+if you are converting a large model, run:
+```bash
+python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --use-large --is_vqa
+```
+Once saved, you can push your converted model with the following snippet:
+```python
+from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
+
+model = Pix2StructForConditionalGeneration.from_pretrained(PATH_TO_SAVE)
+processor = Pix2StructProcessor.from_pretrained(PATH_TO_SAVE)
+
+model.push_to_hub("USERNAME/MODEL_NAME")
+processor.push_to_hub("USERNAME/MODEL_NAME")
+```
+
 
 # Contribution