Improve model card for SLM-SQL: Add GitHub link and usage example

README.md

@@ -1,42 +1,26 @@
 ---
-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) |
+📖[Arxiv Paper](https://arxiv.org/abs/2507.22478) | 🤗[Hugging Face Paper](https://huggingface.co/papers/2507.22478) | 🐙[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
 
 + `July 31, 2025`: Upload model to modelscope and huggingface.
 + `July 30, 2025`: Publish the paper to arxiv
 
-## 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.
+## Abstract
+
+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
 
@@ -55,6 +39,56 @@ 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">
 
+## How to Use
+
+You can easily use this model with the Hugging Face `transformers` library. Below is a general example for inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "cycloneboy/SLM-SQL-1.5B" # Example: You can choose other models from the table below
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16, # or torch.float16, adjust based on your GPU
+    device_map="auto" # Automatically map model to available devices
+)
+model.eval()
+
+# Example prompt for Text-to-SQL
+# Replace this with your natural language query for a specific database schema
+prompt = """
+[Instruction]: Given the following database schema, generate a SQL query that answers the question.
+[Schema]:
+CREATE TABLE Student (StuID INT, Name TEXT, Age INT, Sex TEXT, Major TEXT, Advisor INT, Graduated BOOL);
+CREATE TABLE Course (CrsID INT, Title TEXT, Dept TEXT, Credits INT);
+CREATE TABLE Enrollment (StuID INT, CrsID INT, Grade REAL);
+CREATE TABLE Advisor (AdvID INT, Name TEXT, Dept TEXT);
+[Question]: What is the average age of students who are taking 'Database' course?
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate SQL query
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    num_beams=1, # Adjust for different decoding strategies
+    do_sample=False,
+    temperature=0.0,
+    top_p=1.0,
+    eos_token_id=tokenizer.eos_token_id
+)
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+
+# The output will contain the prompt and the generated SQL.
+# You might need to parse the generated_text to extract only the SQL query.
+```
+
 ## Model
 
 | **Model**                                | Base Model                   | Train Method | Modelscope                                                                                        | HuggingFace                                                                                  |