Create README.md

README.md

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+## Example
+
+The model is by no means a state-of-the-art model, but nevertheless
+produces reasonable image captioning results. It was mainly fine-tuned 
+as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
+
+The model can be used as follows:
+
+```python
+
+import requests
+
+from PIL import Image
+
+from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel
+
+loc = "ydshieh/flax-vit-gpt2-coco-en"
+
+feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
+
+tokenizer = AutoTokenizer.from_pretrained(loc)
+
+model = FlaxVisionEncoderDecoderModel.from_pretrained(loc)
+
+# We will verify our results on an image of cute cats
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+
+with Image.open(requests.get(url, stream=True).raw) as img:
+
+    pixel_values = feature_extractor(images=img, return_tensors="np").pixel_values
+
+def generate_step(pixel_values):
+
+    output_ids = model.generate(pixel_values, max_length=16, num_beams=4).sequences
+
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+
+    preds = [pred.strip() for pred in preds]
+
+    return preds
+
+preds = generate_step(pixel_values)
+
+print(preds)
+
+# should produce
+
+# ['a cat laying on top of a couch next to another cat']
+
+```