Update README.md

README.md

@@ -1,3 +1,34 @@
 ---
 license: apache-2.0
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+
+# OFA-tiny
+This is the **tiny** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple sequence-to-sequence learning framework.
+
+To use it in Transformers, please refer to https://github.com/OFA-Sys/OFA/tree/feature/add_transformers and download the directory of transformers. After installation, you can use it as shown below:
+
+```
+>>> from PIL import Image
+>>> from torchvision import transforms
+>>> from transformers import OFATokenizer, OFAForConditionalGeneration
+
+>>> mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
+>>> resolution = 256
+>>> patch_resize_transform = transforms.Compose([
+        lambda image: image.convert("RGB"),
+        transforms.Resize((resolution, resolution), interpolation=Image.BICUBIC),
+        transforms.ToTensor(), 
+        transforms.Normalize(mean=mean, std=std)
+    ])
+
+>>> model = OFAForConditionalGeneration.from_pretrained(ckpt_dir)
+>>> tokenizer = OFATokenizer.from_pretrained(ckpt_dir)
+
+>>> txt = " what is the description of the image?"
+>>> inputs = tokenizer([txt], max_length=1024, return_tensors="pt")["input_ids"]
+>>> img = Image.open(path_to_image)
+>>> patch_img = patch_resize_transform(img).unsqueeze(0)
+
+>>> gen = model.generate(inputs, patch_img=patch_img, num_beams=4)
+>>> print(tokenizer.decode(gen, skip_special_tokens=True, clean_up_tokenization_spaces=False))
+```