Improve model card: Add code-generation tag and sample usage

This PR enhances the model card by:

- Adding the `code-generation` tag, which helps in better categorization and discoverability of the model's capabilities in generating code artifacts like websites.
- Providing a clear Python code snippet for sample usage with the `transformers` library, making it easier for users to quickly get started with text (code) generation.

README.md

@@ -5,11 +5,13 @@ 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
@@ -30,6 +32,40 @@ The WebGen-LM family of models are as follows:
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b0bfef2f2f9c345b87e673/ADt1JdvKw-IZ_xnS17adL.png)
 
+## Sample Usage
+
+You can use this model with the Hugging Face `transformers` library.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model_id = "luzimu/WebGen-LM-7B" # This model card refers to WebGen-LM-7B
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+# Example for website generation
+user_prompt = "Generate a simple HTML page with a heading 'Hello, World!' and a paragraph of lorem ipsum text."
+messages = [
+    {"role": "user", "content": user_prompt}
+]
+
+# Apply chat template for instruction-following format
+text_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# Generate output
+model_inputs = tokenizer(text_input, return_tensors="pt").to(model.device)
+generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=500, do_sample=True, temperature=0.01, top_k=50, top_p=0.95)
+
+# Decode and print the generated code
+generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(generated_text)
+
+# Example using Hugging Face pipeline for simpler inference
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+result = generator(user_prompt, max_new_tokens=500, do_sample=True, temperature=0.01, top_k=50, top_p=0.95)
+print(result[0]['generated_text'])
+```
 
 ## Citation