Xiyansql QwenCoder 32B 2504

🚀 XiYanSQL-QwenCoder-2504

XiYanSQL-QwenCoder-2504 is our latest SQL generation model, which optimizes upon the previous version and delivers enhanced performance.

🚀 Quick Start

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.

✨ Features

• 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.

📦 Installation

Not provided in the original README, so this section is skipped.

💻 Usage Examples

Basic Usage

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.

# Requirements
# - transformers >= 4.37.0
# - vllm >= 0.7.2

# Prompt Template
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
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
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

📚 Documentation

Important Links

📖Github | 🤖ModelScope | 🌐XiYan-SQL | 🌕析言GBI | 💻Modelscope Space

Model Downloads

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.

🔧 Technical Details

Not provided in the original README, so this section is skipped.

📄 License

The model is released under the Apache-2.0 license.

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!