Improve model card: add GitHub link and usage example

This PR improves the model card for SLM-SQL by adding a prominent link to the GitHub repository and a Python code example for model usage with the `transformers` library. This makes it easier for users to find the code and get started with the model.

A minor formatting fix for percentage signs in the introduction has also been included.

README.md

@@ -1,20 +1,20 @@
 ---
-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) |
-🤖[ModelScope](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) |
+🤗[HuggingFace Collection](https://huggingface.co/collections/cycloneboy/slm-sql-688b02f99f958d7a417658dc) |
+🤖[ModelScope Collection](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) |
+📚[GitHub Repository](https://github.com/CycloneBoy/slm_sql)
 
 ## News
 
@@ -55,6 +55,42 @@ 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">
 
+## Usage
+
+This model can be loaded and used directly with the Hugging Face `transformers` library. Below is a basic example for Text-to-SQL generation.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the tokenizer and model
+model_name = "cycloneboy/SLM-SQL-0.5B" # You can replace with other models from the table below
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.bfloat16)
+
+# Example text-to-SQL query
+# For Text-to-SQL, you might also need to provide schema information depending on the model's training.
+prompt = "Give me the SQL query for customers who placed orders in New York."
+
+# For chat models like Qwen2.5-Coder-0.5B-Instruct, it's often best to use the chat template:
+messages = [
+    {"role": "user", "content": prompt}
+]
+chat_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# Tokenize input
+input_ids = tokenizer(chat_input, return_tensors="pt").input_ids.to(model.device)
+
+# Generate SQL query
+# Adjust generation parameters as needed. Common ones include max_new_tokens, do_sample, temperature, top_p, num_beams
+generated_ids = model.generate(input_ids, max_new_tokens=100, num_beams=1, do_sample=False)
+
+# Decode and print the generated SQL
+# Set skip_special_tokens=True to remove special tokens from the output.
+generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(generated_text)
+```
+
 ## Model
 
 | **Model**                                | Base Model                   | Train Method | Modelscope                                                                                        | HuggingFace                                                                                  |