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README.md

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-license: apache-2.0
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+---
+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 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](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-3B-2502) 🤗[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-3B-2502)|
+|XiYanSQL-QwenCoder-7B  |💻[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-7B-2502) 🤗[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-7B-2502)|
+|XiYanSQL-QwenCoder-14B |💻[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-14B-2502) 🤗[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-14B-2502)|
+|XiYanSQL-QwenCoder-32B |💻[HuggingFace](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-32B-2412) 🤗[Modelscope](https://www.modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-32B-2412)|
+
+
+
+## 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](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.
+
+```
+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!