Update README.md

README.md

@@ -1,3 +1,42 @@
 ---
 license: apache-2.0
 ---
+
+# OFA-base
+This is the **base** 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.
+
+The directory includes 4 files, namely `config.json` which consists of model configuration, `vocab.json` and `merge.txt` for our OFA tokenizer, and lastly `pytorch_model.bin` which consists of model weights. There is no need to worry about the mismatch between Fairseq and transformers, since we have addressed the issue yet. 
+
+To use it in transformers, please refer to https://github.com/OFA-Sys/OFA/tree/feature/add_transformers. Install the transformers and download the models as shown below.
+```
+git clone --single-branch --branch feature/add_transformers https://github.com/OFA-Sys/OFA.git
+pip install OFA/transformers/
+it clone https://huggingface.co/OFA-Sys/OFA-base
+```
+After, refer the path to OFA-base to `ckpt_dir`, and prepare an image for the testing example below. Also, ensure that you have pillow and torchvision in your environment. 
+
+```
+>>> 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_images=patch_img, num_beams=4)
+>>> print(tokenizer.batch_decode(gen, skip_special_tokens=True))
+```