Update README.md

README.md

@@ -30,29 +30,6 @@ The abstract of the paper states that:
 
 # Using the model 
 
-## 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_original_pytorch_to_hf.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")
-```
-
-## Get predictions from the model
-
 You should ask specific questions to the model in order to get consistent generations. Here we are asking the model whether the sum of values that are in a chart are greater than the largest value.
 
 ```python
@@ -73,6 +50,26 @@ print(processor.decode(predictions[0], skip_special_tokens=True))
 
 To run the predictions on GPU, simply add `.to(0)` when creating the model and when getting the inputs (`inputs = inputs.to(0)`)
 
+## 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_original_pytorch_to_hf.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
 
 This model was originally contributed by Fangyu Liu, Francesco Piccinno et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada).