Improve model card: Add prominent GitHub link and sample usage

This PR improves the model card for SLM-SQL by:
- Adding a prominent link to the GitHub repository (`https://github.com/CycloneBoy/slm_sql`) in the "Important Links" section, making it easier for users to find the code.
- Incorporating a clear sample usage example to demonstrate how to load and run the model using the `transformers` library, which significantly enhances user accessibility.

README.md

@@ -1,19 +1,18 @@
 ---
-pipeline_tag: text-generation
 library_name: transformers
 license: cc-by-nc-4.0
+pipeline_tag: text-generation
 tags:
 - text-to-sql
 - reinforcement-learning
 ---
 
-
 # SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
 
 ### Important Links
 
-📖[Arxiv Paper](https://arxiv.org/abs/2507.22478) |
-🤗[HuggingFace](https://huggingface.co/collections/cycloneboy/slm-sql-688b02f99f958d7a417658dc) |
+📖[Arxiv Paper](https://arxiv.org/abs/2507.22478) | 💾[GitHub](https://github.com/CycloneBoy/slm_sql) |
+🤗[HuggingFace Collection](https://huggingface.co/collections/cycloneboy/slm-sql-688b02f99f958d7a417658dc) |
 🤖[ModelScope](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) |
 
 ## News
@@ -55,6 +54,33 @@ Performance Comparison of different Text-to-SQL methods on BIRD dev and test dat
 
 <img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_ablation_study.png"  height="300" alt="slmsql_ablation_study">
 
+## Sample Usage
+
+You can use the model with the `transformers` library. Here's an example:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the tokenizer and model (e.g., SLM-SQL-1.5B)
+model_name = "cycloneboy/SLM-SQL-1.5B" # Adjust this to the specific model you want to use
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+
+# Define the input prompt (natural language question for SQL)
+prompt = "what are the names of all employees?"
+
+# Prepare the input for the model
+input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+
+# Generate the SQL query
+output_ids = model.generate(input_ids, max_new_tokens=100, num_beams=1, do_sample=False)
+generated_sql = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+print("Generated SQL Query:")
+print(generated_sql)
+```
+
 ## Model
 
 | **Model**                                | Base Model                   | Train Method | Modelscope                                                                                        | HuggingFace                                                                                  |