--- title: Agentar-Scale-SQL-Generation-32B canonical_url: "https://www.modelscope.cn/models/AntGroup/Agentar-Scale-SQL-Generation-32B" md_url: "https://www.modelscope.cn/models/AntGroup/Agentar-Scale-SQL-Generation-32B.md" repository: AntGroup/Agentar-Scale-SQL-Generation-32B last_updated: 2025-11-28 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation model_type: - qwen2 architectures: - Qwen2ForCausalLM base_model: - Omni-SQL-32B base_model_relation: finetune parameters: 32.8B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch language: - en inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 468 stars: 17 --- # Agentar-Scale-SQL-Generation-32B > Agentar-Scale-SQL-Generation-32B - AntGroup 在 ModelScope 开源的模型。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… AntGroup/Agentar-Scale-SQL-Generation-32B 是 ModelScope 魔搭社区上的 32.8B 参数text-generation模型，采用 apache-2.0 许可，基于 Omni-SQL-32B 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: AntGroup/Agentar-Scale-SQL-Generation-32B - **License**: apache-2.0 - **Tasks**: text-generation - **Parameters**: 32.8B - **Base model**: Omni-SQL-32B - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Downloads**: 468 - **Stars**: 17 - **Last updated**: 2025-11-28 Source: https://www.modelscope.cn/models/AntGroup/Agentar-Scale-SQL-Generation-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. """ ```` ## Try Our Product We are developing a ChatBI product that transforms complex business data into conversational insights. If you are interested in trying our ChatBI product, please contact us.
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Figure 1: The contact information.
## 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}, } ```