XGenerationLab/XiYanSQL-QwenCoder-3B-2502

Important Links

🤖Github | 🤗ModelScope | 📖XiYan-SQL | 🌕析言GBI | 🌞Modelscope Space

Introduction

We are excited to open source the XiYanSQL-QwenCoder series model, dedicated to advancing the development of LLMs in the text-to-SQL domain. As of now, XiYanSQL-QwenCoder covers four mainstream model sizes: 3B, 7B, 14B, and 32B parameters, to meet the needs of different developers.

• The XiYanSQL-QwenCoder model demonstrates strong performance in SQL generation, with the XiYanSQL-QwenCoder-32B achieving a 69.03% EX score on the BIRD TEST set, setting a new SOTA with a single fine-tuned model. Other models in the series also maintain a leading position at their respective sizes.
• The XiYanSQL-QwenCoder model supports multiple SQL dialects, such as SQLite, PostgreSQL, and MySQL.
• The XiYanSQL-QwenCoder model can be used directly for text-to-SQL tasks or serve as a better starting point for fine-tuning SQL models.

Model Downloads

Model	Download Latest
XiYanSQL-QwenCoder-3B	💻HuggingFace 🤗Modelscope
XiYanSQL-QwenCoder-7B	💻HuggingFace 🤗Modelscope
XiYanSQL-QwenCoder-14B	💻HuggingFace 🤗Modelscope
XiYanSQL-QwenCoder-32B	💻HuggingFace 🤗Modelscope

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 benchmarks in the Text-to-SQL domain.

Model name	BIRD Dev@M-Schema	BIRD Dev@DDL	Spider Test@M-Schema	Spider Test@DDL
Codellama-34b	33.05%	-	67.74%	-
Deepseek-coder-33b	47.52%	44.72%	72.39%	-
TableGPT2	46.35%	47.07%	74.76%	77.28%
Codestral 22b	50.52%	47.00%	78.45%	75.47%
GLM-4-plus	54.37%	-	79.40%	-
Claude35_sonnet-1022	53.32%	50.46%	76.27%	73.04%
Deepseek(v2.5-1210)	55.74%	55.61%	82.08%	80.57%
Gemini-1.5-pro	61.34%	57.89%	85.11%	84.00%
GPT-4o-0806	58.47%	54.82%	82.89%	78.45%
XiYanSQL-QwenCoder-3B	54.11%	53.19%	82.69%	78.85%
XiYanSQL-QwenCoder-7B	59.78%	56.58%	84.86%	80.31%
XiYanSQL-QwenCoder-14B	63.10%	60.37%	85.76%	82.79%
XiYanSQL-QwenCoder-32B	67.01%	63.04%	88.39%	85.46%

Requirements

transformers >= 4.37.0

Quickstart

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 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.

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"""

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "XGenerationLab/XiYanSQL-QwenCoder-32B-2412"
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]

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!