Update README.md

README.md

@@ -32,7 +32,57 @@ VLM2Vec-LlaVa-Next could outperform the baselines and other version of VLM2Vec b
 
 
 ## How to use VLM2Vec-LlaVa-Next
+(More details please refer to our Github repo, here is just a simple demo.)
 
+```python
+from src.model import MMEBModel
+from src.arguments import ModelArguments
+from src.utils import load_processor
+
+import torch
+from transformers import HfArgumentParser, AutoProcessor
+from PIL import Image
+import numpy as np
+
+
+model_args = ModelArguments(
+    model_name='TIGER-Lab/VLM2Vec-LLaVa-Next',
+    pooling='last',
+    normalize=True,
+    model_backbone='llava_next')
+
+processor = load_processor(model_args)
+
+model = MMEBModel.load(model_args)
+model.eval()
+model = model.to('cuda', dtype=torch.bfloat16)
+
+# Image + Text -> Text
+inputs = processor(text='<image> Represent the given image with the following question: What is in the image',
+                   images=Image.open('figures/example.jpg'),
+                   return_tensors="pt")
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+qry_output = model(qry=inputs)["qry_reps"]
+
+string = 'A cat and a dog'
+inputs = processor(text=string,
+                   images=None,
+                   return_tensors="pt")
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+tgt_output = model(tgt=inputs)["tgt_reps"]
+print(string, '=', model.compute_similarity(qry_output, tgt_output))
+## A cat and a dog = tensor([[0.4414]], device='cuda:0', dtype=torch.bfloat16)
+
+string = 'A cat and a tiger'
+inputs = processor(text=string,
+                   images=None,
+                   return_tensors="pt")
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+tgt_output = model(tgt=inputs)["tgt_reps"]
+print(string, '=', model.compute_similarity(qry_output, tgt_output))
+## A cat and a tiger = tensor([[0.3555]], device='cuda:0', dtype=torch.bfloat16)
+
+```
 
 
 ## Citation