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

This PR enhances the model card by:
- Adding the `pipeline_tag: image-text-to-text` to accurately describe the model's functionality (generating text/code from visual feedback and text instructions).
- Specifying `library_name: transformers` based on the `config.json` showing `Qwen2ForCausalLM` and `qwen2` model type, indicating compatibility with the Hugging Face Transformers library.
- Adding a direct link to the GitHub repository: https://github.com/mnluzimu/WebGen-Agent.
- Including a "Sample Usage" section with a `bash` code snippet for single inference, directly extracted from the project's GitHub README, to guide users on how to run the model.

Please review these additions and improvements.

README.md

@@ -1,16 +1,20 @@
 ---
-license: mit
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
 datasets:
 - luzimu/webgen-agent_train_step-grpo
 - luzimu/webgen-agent_train_sft
-base_model:
-- Qwen/Qwen2.5-Coder-7B-Instruct
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
 # WebGen-Agent
 
 WebGen-Agent is an advanced website generation agent designed to autonomously create websites from natural language instructions. It was introduced in the paper [WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning](https://arxiv.org/pdf/2509.22644v1).
 
+Code: https://github.com/mnluzimu/WebGen-Agent
+
 ## Project Overview
 
 WebGen-Agent combines state-of-the-art language models with specialized training techniques to create a powerful website generation tool. The agent can understand natural language instructions specifying appearance and functional requirements, iteratively generate website codebases, and refine them using visual and functional feedback.
@@ -35,26 +39,46 @@ Links to the data and model parameters are as follows:
 
 WebGen-Agent follows an iterative, multi-step paradigm for website generation:
 
-1. **Code Generation**: The agent generates code to create or edit website files based on natural language instructions
-2. **Code Execution**: Dependencies are installed and the website service is started
-3. **Feedback Gathering**: 
-   - A screenshot of the website is captured
-   - A Visual Language Model (VLM) provides appearance feedback and scores
-   - A GUI-agent tests the website functionality and provides functional feedback
-4. **Refinement**: Based on the feedback, the agent continues to improve the website until it meets requirements
+1.  **Code Generation**: The agent generates code to create or edit website files based on natural language instructions
+2.  **Code Execution**: Dependencies are installed and the website service is started
+3.  **Feedback Gathering**: 
+    -   A screenshot of the website is captured
+    -   A Visual Language Model (VLM) provides appearance feedback and scores
+    -   A GUI-agent tests the website functionality and provides functional feedback
+4.  **Refinement**: Based on the feedback, the agent continues to improve the website until it meets requirements
 
 ![WebGen-Agent Workflow](fig/webgen-agent.png)
 
 ## Step-GRPO with Screenshot and GUI-agent Feedback
 
 The Step-GRPO with Screenshot and GUI-agent Feedback approach uses the screenshot and GUI-agent scores inherently produced in the WebGen-Agent workflow as step-level rewards:
-- **Screenshot Score**: Quantifies the visual appeal and aesthetics of the website
-- **GUI-agent Score**: Measures how well the website meets functional requirements
+-   **Screenshot Score**: Quantifies the visual appeal and aesthetics of the website
+-   **GUI-agent Score**: Measures how well the website meets functional requirements
 
 These dual rewards provide dense, reliable process supervision that significantly improves the model's ability to generate high-quality websites.
 
 ![Step-GRPO with Screenshot and GUI-agent Feedback](fig/step-grpo.png)
 
+## Sample Usage
+
+Before running inference, you should rename `.env.template` to `.env` and set the base urls and api keys for the agent-engine LLM and feedback VLM. They can be obtained from any openai-compatible providers such as [openrouter](https://openrouter.ai/), [modelscope](https://www.modelscope.cn/my/overview), [bailian](https://bailian.console.aliyun.com/#/home), and [llmprovider](https://llmprovider.ai/).
+
+You can also deploy open-source VLMs and LLMs by running `src/scripts/deploy_qwenvl_32b.sh` and `src/scripts/deploy.sh`. Scripts for single inference and batch inference can be found at `src/scripts/infer_single.sh` and `src/scripts/infer_batch.sh`.
+
+### Single Inference
+
+```bash
+python src/infer_single.py \
+    --model deepseek-chat \
+    --vlm_model Qwen/Qwen2.5-VL-32B-Instruct \
+    --instruction "Please implement a wheel of fortune website." \
+    --workspace-dir workspaces_root/test \
+    --log-dir service_logs/test \
+    --max-iter 20 \
+    --overwrite \
+    --error-limit 5
+```
+
 ## Citation
 
 If you find our project useful, please cite: