Improve model card with GitHub link and sample usage

This PR enhances the model card by:
- Adding a direct link to the GitHub repository in the "Important Links" section for easier access to the code.
- Clarifying "HuggingFace" and "ModelScope" links as "Collections" in the "Important Links" section.
- Refactoring the "Introduction" section by removing the blockquote formatting and eliminating the redundant sentence about the GitHub release, now that a dedicated link is available.
- Including a practical Python code snippet in a new "Sample Usage" section, demonstrating how to perform Text-to-SQL generation using the `transformers` library, `torch_dtype=torch.bfloat16`, and the model's specific chat template.

README.md

@@ -1,20 +1,17 @@
 ---
-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) |
+📖[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 Collection](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) |
 
 ## News
 
@@ -23,20 +20,7 @@ tags:
 
 ## Introduction
 
-> Large language models (LLMs) have demonstrated strong performance in translating natural language questions into SQL
-> queries (Text-to-SQL). In contrast, small language models (SLMs) ranging from 0.5B to 1.5B parameters currently
-> underperform on Text-to-SQL tasks due to their limited logical reasoning capabilities. However, SLMs offer inherent
-> advantages in inference speed and suitability for edge deployment. To explore their potential in Text-to-SQL
-> applications, we leverage recent advancements in post-training techniques. Specifically, we used the open-source
-> SynSQL-2.5M dataset to construct two derived datasets: SynSQL-Think-916K for SQL generation and
-> SynSQL-Merge-Think-310K
-> for SQL merge revision. We then applied supervised fine-tuning and reinforcement learning-based post-training to the
-> SLM, followed by inference using a corrective self-consistency approach. Experimental results validate the
-> effectiveness
-> and generalizability of our method, SLM-SQL. On the BIRD development set, the five evaluated models achieved an
-> average
-> improvement of 31.4 points. Notably, the 0.5B model reached 56.87\% execution accuracy (EX), while the 1.5B model
-> achieved 67.08\% EX. We will release our dataset, model, and code to github: https://github.com/CycloneBoy/slm_sql.
+Large language models (LLMs) have demonstrated strong performance in translating natural language questions into SQL queries (Text-to-SQL). In contrast, small language models (SLMs) ranging from 0.5B to 1.5B parameters currently underperform on Text-to-SQL tasks due to their limited logical reasoning capabilities. However, SLMs offer inherent advantages in inference speed and suitability for edge deployment. To explore their potential in Text-to-SQL applications, we leverage recent advancements in post-training techniques. Specifically, we used the open-source SynSQL-2.5M dataset to construct two derived datasets: SynSQL-Think-916K for SQL generation and SynSQL-Merge-Think-310K for SQL merge revision. We then applied supervised fine-tuning and reinforcement learning-based post-training to the SLM, followed by inference using a corrective self-consistency approach. Experimental results validate the effectiveness and generalizability of our method, SLM-SQL. On the BIRD development set, the five evaluated models achieved an average improvement of 31.4 points. Notably, the 0.5B model reached 56.87\% execution accuracy (EX), while the 1.5B model achieved 67.08\% EX.
 
 ### Framework
 
@@ -62,7 +46,7 @@ Performance Comparison of different Text-to-SQL methods on BIRD dev and test dat
 | SLM-SQL-Base-0.5B                        | Qwen2.5-Coder-0.5B-Instruct  | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-0.5B)                        | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-0.5B)                        |
 | SLM-SQL-0.5B                             | Qwen2.5-Coder-0.5B-Instruct  | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-0.5B)                             | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-0.5B)                             |
 | CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct | Qwen2.5-Coder-0.5B-Instruct  | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct) | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct) |
-| 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-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)                        |
@@ -71,6 +55,71 @@ Performance Comparison of different Text-to-SQL methods on BIRD dev and test dat
 | 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 )                            |
 | SLM-SQL-Base-1B                          | Llama-3.2-1B-Instruct        | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1B )                         | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1B )                         |
 
+## Sample Usage
+
+This model can be easily loaded and used with the `transformers` library. The following example demonstrates how to perform Text-to-SQL generation.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "cycloneboy/SLM-SQL-0.5B" # You can choose any of the models from the table above
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16, # Use torch.bfloat16 as specified in the model's config
+    device_map="auto" # Automatically maps the model to available devices (e.g., GPU)
+)
+
+# Example SQL schema (simplified for demonstration)
+schema = """
+CREATE TABLE employees (
+    employee_id INT,
+    first_name VARCHAR,
+    last_name VARCHAR,
+    department VARCHAR,
+    salary INT
+);
+"""
+
+# Natural language query
+query = "Show me the first name and last name of employees in the 'Sales' department earning more than 50000."
+
+# Construct the prompt using the model's chat template format
+# The chat template automatically adds system/user tags if available.
+messages = [
+    {"role": "user", "content": f"Translate the following natural language query into SQL:\
+Schema: {schema}\
+Query: {query}"}
+]
+prompt_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device)
+
+# Generate the SQL query
+outputs = model.generate(**inputs, max_new_tokens=256, pad_token_id=tokenizer.eos_token_id)
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Extracting only the generated SQL part (assuming the model responds only with SQL after "### Response:")
+# The model's chat template is `### Instruction:
+...
+### Response:
+...<|EOT|>`
+# We need to trim the input prompt and the <|EOT|> token.
+if "### Response:" in generated_text:
+    sql_start_index = generated_text.find("### Response:") + len("### Response:")
+    generated_sql = generated_text[sql_start_index:].strip()
+    if "<|EOT|>" in generated_sql:
+        generated_sql = generated_sql.split("<|EOT|>")[0].strip()
+else:
+    generated_sql = generated_text # Fallback if response format is unexpected
+
+print(generated_sql)
+
+# Expected output (may vary slightly based on model's exact generation):
+# SELECT first_name, last_name FROM employees WHERE department = 'Sales' AND salary > 50000;
+```
+
 ## Dataset
 
 | **Dataset**                | Modelscope                                                                         | HuggingFace                                                                          |