Improve model card: Add pipeline tag, library name, code link, and sample usage

This PR enhances the model card by:
- Adding `pipeline_tag: image-text-to-text` to the metadata, which improves discoverability and categorization on the Hugging Face Hub.
- Adding `library_name: transformers` to the metadata, enabling the "Use in Transformers" widget and code snippets on the model page.
- Including a direct link to the project's GitHub repository for easier access to the associated code.
- Providing a clear, runnable Python code snippet using the `transformers` library for convenient model inference.

README.md

@@ -1,10 +1,12 @@
 ---
-license: apache-2.0
+base_model:
+- llava-hf/llava-onevision-qwen2-7b-ov-hf
 datasets:
 - Code2Logic/GameQA-140K
 - Code2Logic/GameQA-5K
-base_model:
-- llava-hf/llava-onevision-qwen2-7b-ov-hf
+license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
 ***This model (GameQA-LLaVA-OV-7B) results from training LLaVA-OV-7B with GRPO solely on our [GameQA-5K](https://huggingface.co/datasets/Code2Logic/GameQA-5K) (sampled from the full [GameQA-140K](https://huggingface.co/datasets/Gabriel166/GameQA-140K) dataset).***
@@ -19,8 +21,48 @@ base_model:
 
 This is the first work, to the best of our knowledge, that leverages ***game code*** to synthesize multimodal reasoning data for ***training*** VLMs. Furthermore, when trained with a GRPO strategy solely on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.
 
-[[📖 Paper](https://arxiv.org/abs/2505.13886)] [[🤗 GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [[🤗 GameQA-5K Dataset](https://huggingface.co/datasets/Code2Logic/GameQA-5K)] [[🤗 GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [[🤗 GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [[🤗 GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
+[[📖 Paper](https://arxiv.org/abs/2505.13886)] [[\ud83d\udcbb Code](https://github.com/tongjingqi/Code2Logic)] [[🤗 GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [[🤗 GameQA-5K Dataset](https://huggingface.co/datasets/Code2Logic/GameQA-5K)] [[🤗 GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [[🤗 GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [[\ud83e\udd17 GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
 
 ## News
 
-* We've open-sourced the ***three*** models trained with GRPO on GameQA on [Huggingface](https://huggingface.co/Code2Logic).
+* We've open-sourced the ***three*** models trained with GRPO on GameQA on [Huggingface](https://huggingface.co/Code2Logic).
+
+## Usage
+
+This model is compatible with the `transformers` library. Here's how to use it for image-to-text generation:
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+model_id = "Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf"
+
+# Load processor and model
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16).to("cuda")
+
+# Load your image (replace with an actual image path or PIL Image object)
+# Example: a screenshot of a GUI for a typical use case of this model
+image = Image.open("your_gui_screenshot.jpg")
+
+# Prepare your text prompt. The model is designed for multimodal tasks,
+# so typical inputs involve both an image and a text query.
+prompt = "What is highlighted in the screenshot? Provide a concise description."
+
+# Construct the chat history format required by the model
+messages = [
+    {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt}]}
+]
+chat_prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# Process inputs for the model
+inputs = processor(text=chat_prompt, images=image, return_tensors="pt").to(model.device)
+
+# Generate response
+with torch.no_grad():
+    generated_ids = model.generate(**inputs, max_new_tokens=100) # Adjust max_new_tokens as needed
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+print(generated_text)
+```