Improve model card

This PR adds more metadata to the model card, like the library name, pipeline tag, and base model.

Feel free to tweak if required.

README.md

@@ -1,37 +1,48 @@
+---
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model:
+- Hon-Wong/VoRA-7B-Base
+datasets:
+- Hon-Wong/VoRA-Recap-29M
+---
+
 # VoRA
 * [ArXiv Paper](https://arxiv.org/abs/2503.20680)
 * [Github](https://github.com/Hon-Wong/VoRA)
 
 ## Quickstart
 
+The model can be used as follows:
+
 ```python
-    import torch
-    from transformers import AutoProcessor, AutoModelForCausalLM
-    model_name = "Hon-Wong/VoRA-7B-Instruct"
-    processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
-    conversation = [
-        {
-            "role":"user",
-            "content":[
-                {
-                    "type":"image",
-                    "url": "{image path or url}"
-                },
-                {
-                    "type":"text",
-                    "text":"<image> Describe this image."
-                }
-            ]
-        }
-    ]
-    model_inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=True, return_tensors='pt', return_dict=True).to(model.device)
-    gen_kwargs = {"max_new_tokens": 1024, "eos_token_id": processor.tokenizer.eos_token_id}
-    
-    with torch.inference_mode():
-        outputs = model.generate(model_inputs, **gen_kwargs)
-        output_text = processor.tokenizer.batch_decode(
-            outputs, skip_special_tokens=True
-        )
-        print(output_text)
+import torch
+from transformers import AutoProcessor, AutoModelForCausalLM
+model_name = "Hon-Wong/VoRA-7B-Instruct"
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+conversation = [
+    {
+        "role":"user",
+        "content":[
+            {
+                "type":"image",
+                "url": "{image path or url}"
+            },
+            {
+                "type":"text",
+                "text":"<image> Describe this image."
+            }
+        ]
+    }
+]
+model_inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=True, return_tensors='pt', return_dict=True).to(model.device)
+gen_kwargs = {"max_new_tokens": 1024, "eos_token_id": processor.tokenizer.eos_token_id}
+
+with torch.inference_mode():
+    outputs = model.generate(model_inputs, **gen_kwargs)
+    output_text = processor.tokenizer.batch_decode(
+        outputs, skip_special_tokens=True
+    )
+    print(output_text)
 ```