Enhance model card: Add GitHub link and usage example

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) |
+\ud83d\udcbb[GitHub Repository](https://github.com/CycloneBoy/slm_sql) |
+🤗[HuggingFace Collection](https://huggingface.co/collections/cycloneboy/slm-sql-688b02f99f958d7a417658dc) |
+🤖[ModelScope Collection](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) |
 
 ## News
 
@@ -49,7 +49,7 @@ tags:
 
 <img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_bird_main.png"  height="500" alt="slmsql_bird_main">
 
-<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_spider_main.png"  height="500" alt="slmsql_spider_main">
+<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_spider_main.png"  height="500" alt="slm_sql_spider_main">
 
 Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset.
 
@@ -65,7 +65,7 @@ Performance Comparison of different Text-to-SQL methods on BIRD dev and test dat
 | SLM-SQL-Base-1.5B                        | Qwen2.5-Coder-1.5B-Instruct  | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1.5B)                        | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1.5B)                        |
 | SLM-SQL-1.5B                             | Qwen2.5-Coder-1.5B-Instruct  | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-1.5B)                             | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-1.5B)                             |
 | CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct | Qwen2.5-Coder-1.5B-Instruct  | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct) | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct) |
-| SLM-SQL-Base-0.6B                        | Qwen3-0.6B                   | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-0.6B)                        | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-0.6B)                        |
+| SLM-SQL-Base-0.6B                        | Qwen3-0.6B                   | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-0.6B)                        | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-0.6B)                        |\
 | SLM-SQL-0.6B                             | Qwen3-0.6B                   | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-0.6B)                             | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-0.6B)                             |
 | SLM-SQL-Base-1.3B                        | deepseek-coder-1.3b-instruct | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1.3B )                       | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1.3B )                       |
 | SLM-SQL-1.3B                             | deepseek-coder-1.3b-instruct | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-1.3B )                            | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-1.3B )                            |
@@ -79,6 +79,44 @@ Performance Comparison of different Text-to-SQL methods on BIRD dev and test dat
 | SynsQL-Merge-Think-310k    | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/SynsQL-Merge-Think-310k) | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/SynsQL-Merge-Think-310k) |
 | bird train and dev dataset | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/bird_train)              | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/bird_train)              |
 
+## Usage
+
+You can easily load the model and use it for text-to-SQL generation with the Hugging Face `transformers` library. Here is an example:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "cycloneboy/SLM-SQL-0.5B" 
+# Or choose another model from the table above, e.g., "cycloneboy/SLM-SQL-1.5B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.bfloat16)
+
+# Example prompt for text-to-SQL
+# Replace 'Your_Database_Schema_Here' with your actual database schema/DDL if needed
+# The model might expect a specific prompt format based on its training,
+# refer to the original GitHub repository for detailed prompting instructions.
+prompt = "Please give me the names of all employees who work in the 'Sales' department."
+
+# Apply chat template if available, or just format the prompt directly
+if hasattr(tokenizer, 'apply_chat_template') and tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+else:
+    input_text = prompt
+
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+# Generate SQL query
+outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.7, do_sample=True, top_p=0.95)
+
+# Decode and print the generated SQL
+generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(f"Generated SQL:
+{generated_sql}")
+```
+
 ## TODO
 
 - [ ] Release inference code