Update README.md

README.md

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 license: apache-2.0
 base_model:
 - seeklhy/OmniSQL-32B
----
+---
+
+
+## Important Links
+
+[![Paper](https://img.shields.io/badge/paper-arXiv-red)](https://arxiv.org/abs/2509.24403)
+[![GitHub](https://img.shields.io/badge/GitHub-Repo-blue.svg)](https://github.com/antgroup/Agentar-Scale-SQL)
+[![Leaderboard](https://img.shields.io/badge/BIRD%20Leaderboard-%231-brightgreen)](https://bird-bench.github.io/)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/collections/antgroup/agentar-scale-sql)
+[![ModelScope](https://img.shields.io/badge/ModelScope-Models-blue)](https://modelscope.cn/collections/Agentar-Scale-SQL-0c368e98f73f41)
+
+## Introduction
+
+We are excited to release the **Agentar-Scale-SQL-Generation-32B**, the core **Reasoning SQL Generator** used in our SOTA framework, **Agentar-Scale-SQL**. Our framework achieved **81.67% execution accuracy** on the challenging BIRD benchmark, ranking first on the official leaderboard.
+
+This model is a key component of our "Orchestrated Test-Time Scaling" strategy and has several key features:
+
+  - **Base Model:** It is fine-tuned from `Omni-SQL-32B`.
+  - **RL-Enhanced Reasoning:** The model was further trained using an execution-grounded **Reinforcement Learning** framework (GRPO) to enhance its intrinsic reasoning capabilities.
+  - **Deep Reasoning:** It is engineered to conduct deep, step-by-step reasoning and construct complex, high-accuracy SQL queries.
+
+This model is one of the two main generators in the `Agentar-Scale-SQL` framework's "Diverse Synthesis" step, working in parallel with an ICL generator to produce a robust pool of SQL candidates.
+
+## Model Downloads
+
+| **Model**                         | **Role**       |                      
+|-----------------------------------|----------------|
+| **Agentar-Scale-SQL-Generation-32B**  | **SQL Generator**  |
+| Agentar-Scale-SQL-Selection-32B | SQL Selector |
+
+## Performance
+
+The performance metrics below reflect the **entire Agentar-Scale-SQL framework**, which uses this Generation model as a key component. The results demonstrate our SOTA performance on the BIRD benchmark.
+
+| Methods                      | EX (Dev) | **EX (Test)** | R-VES (%) |
+|:-----------------------------|:---:|:---:|:---------:|
+| **Agentar-Scale-SQL (Ours)** | **74.90** | **81.67** | **77.00** |
+| AskData + GPT-4o             | 76.14 | 80.88 |   76.24   |
+| LongData-SQL                 | 74.32 | 77.53 |   71.89   |
+| CHASE-SQL + Gemini           | 74.90 | 76.02 |   69.94   |
+| JoyDataAgent-SQL             | 74.25 | 75.74 |   70.16   |
+| TCDataAgent-SQL              | 74.12 | 75.74 |     -     |
+| Contextual-SQL               | 73.50 | 75.63 |   70.02   |
+| XiYan-SQL                    | 73.34 | 75.63 |   71.41   |
+
+
+## Prompt Template
+
+````python
+PROMPT_TEMPLATE = """Task Overview:
+You are a data science expert. Below, you are provided with a database schema and a natural language question. Your task is to understand the schema and generate a valid SQL query to answer the question.
+
+Database Engine:
+{{ dialect }}
+
+Database Schema:
+{{ db_schemas }}
+This schema describes the database's structure, including tables, columns, primary keys, foreign keys, and any relevant relationships or constraints.
+{% if matched_contents %}
+Matched contents:
+{{ matched_contents }}
+Matched contents presents values related to the question, together with their source table and column, for your reference in SQL generation.
+{% endif %}
+Question:
+{%- if hint %}
+{{ hint }}
+{{ question }}
+{%- else %}
+{{ question }}
+{%- endif %}
+
+Instructions:
+- If Matched contents is provided, you can use it as reference when generating the SQL query.
+- Make sure you only output the information that is asked in the question. If the question asks for a specific column, make sure to only include that column in the SELECT clause, nothing more.
+- The generated query should return all of the information asked in the question without any missing or extra information.
+- Before generating the final SQL query, please think through the steps of how to write the query.
+
+Output Format:
+In your answer, please enclose the generated SQL query in a code block:
+```sql
+-- Your SQL query
+```
+
+Take a deep breath and think step by step to find the correct SQL query.
+"""
+````
+
+## Acknowledgments
+
+If you find our work useful, please cite the Agentar-Scale-SQL paper:
+
+```bibtex
+@misc{wang2025agentarscalesqladvancingtexttosqlorchestrated,
+      title={Agentar-Scale-SQL: Advancing Text-to-SQL through Orchestrated Test-Time Scaling}, 
+      author={Pengfei Wang and Baolin Sun and Xuemei Dong and Yaxun Dai and Hongwei Yuan and Mengdie Chu and Yingqi Gao and Xiang Qi and Peng Zhang and Ying Yan},
+      year={2025},
+      eprint={2509.24403},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2509.24403}, 
+}
+```
+