update readme for demo

README.md

@@ -1,3 +1,56 @@
 ---
 license: apache-2.0
 ---
+
+# OFA-base
+This is the **huge** 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/
+git clone https://huggingface.co/OFA-Sys/OFA-huge
+```
+After, refer the path to OFA-huge 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, OFAModel
+>>> from generate import sequence_generator
+
+>>> 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 = OFAModel.from_pretrained(ckpt_dir)
+>>> tokenizer = OFATokenizer.from_pretrained(ckpt_dir)
+
+>>> txt = " what does the image describe?"
+>>> inputs = tokenizer([txt], return_tensors="pt").input_ids
+>>> img = Image.open(path_to_image)
+>>> patch_img = patch_resize_transform(img).unsqueeze(0)
+
+
+>>> # using the generator of fairseq version
+>>> generator = sequence_generator.SequenceGenerator(tokenizer=tokenizer,beam_size=5,
+                                                                      max_len_b=16,
+                                                                      min_len=0,
+                                                                      no_repeat_ngram_size=3) # using the generator of fairseq version
+>>> data = {}
+>>> data["net_input"] = {"input_ids": inputs, 'patch_images': patch_img, 'patch_masks':torch.tensor([True])}
+>>> gen_output = generator.generate([model], data)
+>>> gen = [gen_output[i][0]["tokens"] for i in range(len(gen_output))]
+
+>>> # using the generator of huggingface version
+>>> gen = model.generate(inputs, patch_images=patch_img, num_beams=5, no_repeat_ngram_size=3) 
+
+>>> print(tokenizer.batch_decode(gen, skip_special_tokens=True))
+```