diff --git a/website/docs/projects/vilt.md b/website/docs/projects/vilt.md
index 4a16923fa..7da003e1c 100644
--- a/website/docs/projects/vilt.md
+++ b/website/docs/projects/vilt.md
@@ -45,3 +45,103 @@ To pretrain a ViLT model from scratch on the COCO dataset,
 ```
 mmf_run config=projects/vilt/configs/masked_coco/pretrain.yaml run_type=train_val dataset=masked_coco model=vilt
 ```
+
+## Using the ViLT model from code
+Here is an example of running the ViLT model from code, to do visual question answering (vqa) on a raw image and text.
+The forward pass takes ~15ms which is very fast compared to UNITER's ~600ms.
+
+```python
+from argparse import Namespace
+
+import torch
+from mmf.common.sample import SampleList
+from mmf.datasets.processors.bert_processors import VILTTextTokenizer
+from mmf.datasets.processors.image_processors import VILTImageProcessor
+from mmf.utils.build import build_model
+from mmf.utils.configuration import Configuration, load_yaml
+from mmf.utils.general import get_current_device
+from mmf.utils.text import VocabDict
+from omegaconf import OmegaConf
+from PIL import Image
+```
+
+A way to make model configs and instantiate the ViLT model.
+```python
+# make model config for vilt vqa2
+model_name = "vilt"
+config_args = Namespace(
+    config_override=None,
+    opts=["model=vilt", "dataset=vqa2", "config=configs/defaults.yaml"],
+)
+default_config = Configuration(config_args).get_config()
+model_vqa_config = load_yaml(
+    "/private/home/your/path/to/mmf/projects/vilt/configs/vqa2/defaults.yaml"
+)
+config = OmegaConf.merge(default_config, model_vqa_config)
+OmegaConf.resolve(config)
+model_config = config.model_config[model_name]
+model_config.model = model_name
+vilt_model = build_model(model_config)
+```
+
+Load model weights, `model_checkpoint_path` is the model checkpoint downloaded at model zoo path `vilt.vqa`,
+with current url `s3://dl.fbaipublicfiles.com/mmf/data/models/vilt/vilt.finetuned.vqa2.tar.gz`
+```python
+# build model and load weights
+model_checkpoint_path = './vilt_vqa2.pth'
+state_dict = torch.load(model_checkpoint_path)
+vilt_model.load_state_dict(state_dict, strict=False)
+vilt_model.eval()
+vilt_model = vilt_model.to(get_current_device())
+```
+
+Prepare input image and text.
+This example is using an image of a man with a hat kissing his daughter.
+The text is the question posed to the ViLT model for visual question answering.
+```python
+# get image input
+image_processor = VILTImageProcessor({"size": [384, 384]})
+image_path = "./kissing_image.jpg"
+raw_img = Image.open(image_path).convert("RGB")
+image = image_processor(raw_img)
+
+# get text input
+text_tokenizer = VILTTextTokenizer({})
+question = "What is on his head?"
+processed_text_dict = text_tokenizer({"text": question})
+```
+
+Wrap everything up in a sample list as expected by the ViLT BaseModel.
+```python
+# make batch inputs
+sample_dict = {**processed_text_dict, "image": image}
+sample_dict = {
+    k: v.unsqueeze(0) for k, v in sample_dict.items() if isinstance(v, torch.Tensor)
+}
+sample_dict["targets"] = torch.zeros((1, 3129))
+sample_dict["targets"][0,1358] = 1
+sample_dict["dataset_name"] = "vqa2"
+sample_dict["dataset_type"] = "test"
+sample_list = SampleList(sample_dict).to(get_current_device())
+```
+
+Load the vqa answer -> word string map to understand what it says!
+Currently file url at `s3://dl.fbaipublicfiles.com/mmf/data/datasets/vqa2/defaults/extras/vocabs/answers_vqa.txt`
+```python
+# load vqa2 id -> answers
+vocab_file_path = "/private/home/path/to/answers_vqa.txt"
+answer_vocab = VocabDict(vocab_file_path)
+```
+
+And heres the part you've been waiting for!
+```python
+# do prediction
+with torch.no_grad():
+    vqa_logits = vilt_model(sample_list)["scores"]
+    answer_id = vqa_logits.argmax().item()
+    answer = answer_vocab.idx2word(answer_id)
+    print(chr(27) + "[2J") # clear the terminal
+    print(f"{question}: {answer}")
+```
+
+Expected output `What is on his head?: hat`