Improve model card: Add `code-generation` tag and usage example

This PR enhances the model card by:
- Adding the `code-generation` tag to the metadata for improved discoverability on the Hub, accurately reflecting the model's primary function of generating website code.
- Including a Python code snippet to demonstrate how to use the model with the `transformers` library for generating interactive and functional websites.
- Adding a descriptive sentence at the beginning of the model card to clarify the model's purpose.

The existing arXiv paper link has been retained as per the guidelines.

README.md

@@ -5,16 +5,18 @@ datasets:
 - luzimu/WebGen-Bench
 language:
 - en
+library_name: transformers
 license: mit
 metrics:
 - accuracy
 pipeline_tag: text-generation
-library_name: transformers
+tags:
+- code-generation
 ---
 
 # WebGen-LM
 
-WebGen-LM is trained using the Bolt.diy trajectories generated from a subset of the training set of WebGen-Bench (🤗 [luzimu/WebGen-Bench](https://huggingface.co/datasets/luzimu/WebGen-Bench)). It has been introduced in the paper [WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch](https://arxiv.org/abs/2505.03733).
+WebGen-LM is a code language model specifically trained for generating interactive and functional websites from scratch. It is trained using the Bolt.diy trajectories generated from a subset of the training set of WebGen-Bench (🤗 [luzimu/WebGen-Bench](https://huggingface.co/datasets/luzimu/WebGen-Bench)). It has been introduced in the paper [WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch](https://arxiv.org/abs/2505.03733).
 
 The training data and code can be found at [WebGen-Bench (Github)](https://github.com/mnluzimu/WebGen-Bench).
 
@@ -30,6 +32,69 @@ The WebGen-LM family of models are as follows:
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b0bfef2f2f9c345b87e673/ADt1JdvKw-IZ_xnS17adL.png)
 
+## Usage
+
+You can use `WebGen-LM` with the `transformers` library to generate website code.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "luzimu/WebGen-LM-32B" # You can also use WebGen-LM-7B or WebGen-LM-14B
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Example for website generation
+prompt = """Generate the complete HTML, CSS, and JavaScript code for a responsive website.
+The website should be a simple landing page for a coffee shop.
+It needs:
+1. A navigation bar at the top with "Home", "Menu", "About Us", and "Contact" links.
+2. A hero section with a background image, a title "Brewing Perfection", and a call-to-action button "View Our Menu".
+3. A menu section displaying at least 3 coffee items with their names and prices.
+4. An "About Us" section with a brief description of the coffee shop.
+5. A "Contact" section with an address, phone number, and a simple contact form (Name, Email, Message, Submit button).
+6. Basic responsive design for mobile views.
+"""
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=2048, # Adjust as needed for full website code
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    repetition_penalty=1.05,
+)
+
+# Decode the generated output, skipping special tokens
+response = tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True)[0]
+# The response will contain the full conversation history including the input prompt.
+# To get only the newly generated text, you might need to slice it or use the appropriate
+# tokenizer behavior based on how apply_chat_template adds prompt.
+# For simplicity, if the model just appends to the prompt, direct decode might suffice.
+# A more robust approach might be:
+# generated_text_only = tokenizer.decode(generated_ids[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True)
+print(response)
+
+# You might need to parse the output to separate HTML, CSS, and JS if the model outputs a combined file.
+# For example, look for specific markers like <html>, <style>, <script>
+```
 
 ## Citation