add readme

README.md

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+---
+license: apache-2.0
+---
+
+# OFA-tiny
+
+## Introduction
+This is the **tiny** version of OFA pretrained model finetuned on vqaV2. 
+
+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.
+
+
+## How to use
+Download the models as shown below.
+```bash
+git clone https://github.com/sohananisetty/OFA_VQA.git
+git clone https://huggingface.co/SohanAnisetty/ofa-vqa-tiny
+```
+
+After, refer the path to ofa-vqa-tiny to `ckpt_dir`, and prepare an image for the testing example below. 
+
+```python
+>>> from PIL import Image
+>>> from torchvision import transforms
+>>> from transformers import OFATokenizer, OFAModelForVQA
+
+>>> 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)
+    ])
+
+
+>>> 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)
+
+
+>>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=False)
+>>> 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))
+```