---
license: apache-2.0
---
### Important Links

📖[Github](https://github.com/XGenerationLab/XiYanSQL-QwenCoder) |
🤖[ModelScope](https://modelscope.cn/collections/XiYanSQL-Models-4483337b614241) |
🌐[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 update our new XiYanSQL-QwenCoder series model, demonstrating improvements over its predecessor in some key features.
- The new XiYanSQL-QwenCoder model applies the merits of GRPO training strategy without thinking process, maintaining high efficiency and accuracy in SQL generation.
- The new XiYanSQL-QwenCoder model keeps its great performance in various benchmarks, including BIRD, Spider and DW benchmarks which will be released in the future.
- The new XiYanSQL-QwenCoder model demonstrates better generalization than its predecessor, especially in different dialects and out-of-domain datasets.


## Model Downloads


| **Model** | **Download Latest**                                                                                                                                                            |
|-----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|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-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%       |
| Gemini-2.5-Pro               |  UNK   |      67.21%       |    63.43%    |        88.29%        |     86.27%      |         63.16%         |      65.37%       |
| 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%         |        xx%        |
| 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%        |       xx%       |         31.48%         |      36.17%       |
| OmniSQL-32B                  |  32B   |      60.37%       |    55.87%    |        85.16%        |     83.19%      |         38.19%         |      42.34%       |
| 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%       |


## Requirements

transformers >= 4.37.0
vllm >= 0.7.2

## 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.

### 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-2502"
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-2502"
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=2048
)

## 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
```


## 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!