--- title: XiYanSQL-QwenCoder-32B-2504 canonical_url: "https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-32B-2504" md_url: "https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-32B-2504.md" repository: XGenerationLab/XiYanSQL-QwenCoder-32B-2504 last_updated: 2026-04-13 license: "Apache License 2.0" pipeline_tag: text-generation tasks: - text-generation model_type: - qwen2 architectures: - Qwen2ForCausalLM base_model: - XGenerationLab/XiYanSQL-QwenCoder-32B-2412 base_model_relation: adapter parameters: 32.8B tensor_type: - BF16 library_name: - pytorch - transformer - safetensors frameworks: - Pytorch language: - en - zh inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" supports_inference: txt2txt downloads: 4710 stars: 39 tags: - NL2SQL - SQL - Text-to-SQL --- # XiYanSQL-QwenCoder-32B-2504 > XiYanSQL-QwenCoder-32B-2504 - XGenerationLab 在 ModelScope 开源的模型。Important Links 📖Github | 🤗HuggingFace | 🌐XiYan-SQL | 🌕析言GBI | 💻Modelscope Space XGenerationLab/XiYanSQL-QwenCoder-32B-2504 是 ModelScope 魔搭社区上的 32.8B 参数text-generation模型，采用 Apache License 2.0 许可，基于 XGenerationLab/XiYanSQL-QwenCoder-32B-2412 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署，并支持在线推理（txt2txt）。 - **Repository**: XGenerationLab/XiYanSQL-QwenCoder-32B-2504 - **License**: Apache License 2.0 - **Tasks**: text-generation - **Parameters**: 32.8B - **Base model**: XGenerationLab/XiYanSQL-QwenCoder-32B-2412 - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Online inference**: txt2txt - **Tags**: NL2SQL, SQL, Text-to-SQL - **Downloads**: 4710 - **Stars**: 39 - **Last updated**: 2026-04-13 Source: https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-32B-2504 --- ### Important Links 📖[Github](https://github.com/XGenerationLab/XiYanSQL-QwenCoder) | 🤗[HuggingFace](https://huggingface.co/collections/XGenerationLab/xiyansql-models-67c9844307b49f87436808fc) | 🌐[XiYan-SQL](https://github.com/XGenerationLab/XiYan-SQL) | 🌕[析言GBI](https://bailian.console.aliyun.com/xiyan) | 💻[Modelscope Space](https://www.modelscope.cn/studios/XGenerationLab/XiYanSQL-QwenCoder-32B) ## Introduction We are excited to release the **XiYanSQL-QwenCoder-2504** version, our latest SQL generation model. This version continues to optimize upon the previous version, delivering enhanced performance. - Our model incorporates important explorations combining **fine-tuning and GRPO training**, leveraging the post-training strategies of GRPO without a thinking process, achieving both efficiency and accuracy in SQL generation. - It demonstrates **impressive performance** and supports **multiple dialects**, ready to use out of the box. - Improved generalization capabilities, excelling on different dialects and **out-of-domain datasets**. In this evaluation, we have also added **a real-world SQL benchmark (the DW test set)**, which serves as an important internal evaluation baseline. This test set includes thousands of complex queries from real scenarios in both PostgreSQL and MySQL dialects, effectively reflecting the model's performance across multiple dialects and out-of-domain data. ## Model Downloads | **Model** | **Download Latest** | |-----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |XiYanSQL-QwenCoder-3B | 🤗[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-3B-2504) 🤖[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-3B-2504) | |XiYanSQL-QwenCoder-7B | 🤗[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-7B-2504) 🤖[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-7B-2504) | |XiYanSQL-QwenCoder-14B | 🤗[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-14B-2504) 🤖[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-14B-2504) | |XiYanSQL-QwenCoder-32B | 🤗[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-32B-2504) 🤖[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-32B-2504) | ## Performance The XiYanSQL-QwenCoder models, as multi-dialect SQL base models, demonstrating robust SQL generation capabilities. The following presents the evaluation results at the time of release. We conducted a comprehensive evaluation of the model's performance under two schema formats, M-Schema, and original DDL, using the BIRD and Spider as SQLite benchmarks in the Text-to-SQL domain, as well as DW benchmarks for PostgreSQL and MySQL dialects. | Model name | Size | BIRD Dev@M-Schema | BIRD Dev@DDL | Spider Test@M-Schema | Spider Test@DDL | DW PostgreSQL@M-Schema | DW MySQL@M-Schema | |------------------------------|:----:|:-----------------:|:------------:|:--------------------:|:---------------:|:----------------------:|:-----------------:| | GPT-4o-0806 | UNK | 58.47% | 54.82% | 82.89% | 78.45% | 46.79% | 57.77% | | GPT-4.1-0414 | UNK | 59.39% | 54.11% | 84.45% | 79.86% | 54.29% | 63.18% | | Claude3.5-sonnet-1022 | UNK | 53.32% | 50.46% | 76.27% | 73.04% | 55.22% | 52.84% | | Claude3.7-sonnet | UNK | 54.82% | 49.22% | 78.04% | 74.66% | 53.23% | 54.61% | | Gemini-1.5-Pro | UNK | 61.34% | 57.89% | 85.11% | 84.00% | 52.78% | 62.78% | | DeepSeek-V2.5-1210 | 236B | 55.74% | 55.61% | 82.08% | 80.57% | 45.74% | 52.18% | | DeepSeek-V3 | 685B | 59.58% | 56.71% | 81.52% | 79.91% | 52.56% | 55.95% | | DeepSeek-R1 | 685B | 58.15% | 55.61% | 80.72% | 78.85% | 60.56% | 62.00% | | DeepSeek-R1-Distill-Qwen-32B | 32B | 50.65% | 48.31% | 78.65% | 77.33% | 37.22% | 44.72% | | Deepseek-Coder-33B-Instruct | 33B | 47.52% | 44.72% | 72.39% | 62.0% | 31.48% | 36.17% | | OmniSQL-32B | 32B | 60.37% | 55.87% | 85.16% | 83.19% | 38.19% | 42.34% | | XiYanSQL-QwenCoder-3B-2502 | 3B | 53.52% | 52.54% | 83.34% | 79.10% | 34.75% | 35.62% | | XiYanSQL-QwenCoder-3B-2504 | 3B | 55.08% | 52.09% | 84.10% | 80.57% | 36.65% | 37.63% | | XiYanSQL-QwenCoder-7B-2502 | 7B | 59.65% | 56.32% | 84.15% | 80.01% | 39.38% | 42.10% | | XiYanSQL-QwenCoder-7B-2504 | 7B | 62.13% | 57.43% | 85.97% | 82.48% | 42.08% | 44.67% | | XiYanSQL-QwenCoder-14B-2502 | 14B | 63.23% | 60.10% | 85.31% | 82.84% | 38.51% | 41.62% | | XiYanSQL-QwenCoder-14B-2504 | 14B | 65.32% | 60.17% | 86.82% | 83.75% | 40.52% | 44.60% | | XiYanSQL-QwenCoder-32B-2412 | 32B | 67.07% | 63.04% | 88.39% | 85.46% | 45.07% | 52.84% | | XiYanSQL-QwenCoder-32B-2504 | 32B | 67.14% | 62.26% | 89.20% | 86.17% | 53.52% | 57.74% | ## Quickstart with Transformers and vLLM Here is a simple code snippet for quickly using **XiYanSQL-QwenCoder** model. We provide a Chinese version of the prompt, and you just need to replace the placeholders for "question," "db_schema," and "evidence" to get started. We recommend using our [M-Schema](https://github.com/XGenerationLab/M-Schema) format for the schema; other formats such as DDL are also acceptable, but they may affect performance. Currently, we mainly support mainstream dialects like SQLite, PostgreSQL, and MySQL. ### Requirements - transformers >= 4.37.0 - vllm >= 0.7.2 ### Prompt Template ```python nl2sqlite_template_cn = """你是一名{dialect}专家，现在需要阅读并理解下面的【数据库schema】描述，以及可能用到的【参考信息】，并运用{dialect}知识生成sql语句回答【用户问题】。 【用户问题】 {question} 【数据库schema】 {db_schema} 【参考信息】 {evidence} 【用户问题】 {question} ```sql""" ``` ### Inference with Transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "XGenerationLab/XiYanSQL-QwenCoder-32B-2504" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) ## dialects -> ['SQLite', 'PostgreSQL', 'MySQL'] prompt = nl2sqlite_template_cn.format(dialect="", db_schema="", question="", evidence="") message = [{'role': 'user', 'content': prompt}] text = tokenizer.apply_chat_template( message, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, max_new_tokens=1024, temperature=0.1, top_p=0.8, do_sample=True, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Inference with vLLM ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_path = "XGenerationLab/XiYanSQL-QwenCoder-32B-2504" llm = LLM(model=model_path, tensor_parallel_size=8) tokenizer = AutoTokenizer.from_pretrained(model_path) sampling_params = SamplingParams( n=1, temperature=0.1, max_tokens=1024 ) ## dialects -> ['SQLite', 'PostgreSQL', 'MySQL'] prompt = nl2sqlite_template_cn.format(dialect="", db_schema="", question="", evidence="") message = [{'role': 'user', 'content': prompt}] text = tokenizer.apply_chat_template( message, tokenize=False, add_generation_prompt=True ) outputs = llm.generate([text], sampling_params=sampling_params) response = outputs[0].outputs[0].text ``` ## Contact us: If you are interested in our research or products, please feel free to contact us. #### Contact Information: Yifu Liu, zhencang.lyf@alibaba-inc.com #### Join Our DingTalk Group Ding Group钉钉群 ## Acknowledgments If you find our work useful, please give us a citation or a like, so we can make a greater contribution to the open-source community! ```bibtex @article{XiYanSQL, author={Liu, Yifu and Zhu, Yin and Gao, Yingqi and Luo, Zhiling and Li, Xiaoxia and Shi, Xiaorong and Hong, Yuntao and Gao, Jinyang and Li, Yu and Ding, Bolin and Zhou, Jingren}, journal={IEEE Transactions on Knowledge and Data Engineering}, title={XiYan-SQL: A Novel Multi-Generator Framework for Text-to-SQL}, year={2026}, volume={}, number={}, pages={1-14}, doi={10.1109/TKDE.2026.3657851} } ```