Improve model card: Add pipeline tag, library name, and GitHub link (#1)

- Improve model card: Add pipeline tag, library name, and GitHub link (f76404ec170b63d830ef8511f38ff3bf3b5aaa17)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,16 +1,20 @@
 ---
-license: mit
+base_model:
+- Qwen/Qwen3-8B
 datasets:
 - luzimu/webgen-agent_train_step-grpo
 - luzimu/webgen-agent_train_sft
-base_model:
-- Qwen/Qwen3-8B
+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.
@@ -35,25 +39,25 @@ 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**: 
+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
+4.  **Refinement**: Based on the feedback, the agent continues to improve the website until it meets requirements
 
-![WebGen-Agent Workflow](fig/webgen-agent.png)
+![WebGen-Agent Workflow](https://github.com/mnluzimu/WebGen-Agent/raw/main/paper/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)
+![Step-GRPO with Screenshot and GUI-agent Feedback](https://github.com/mnluzimu/WebGen-Agent/raw/main/paper/step-grpo.png)
 
 ## Citation