Update README.md

README.md

@@ -1,31 +1,32 @@
 ---
-license: apache-2.0
+frameworks:
+- Pytorch
+tasks:
+- text-generation
 base_model:
-- XGenerationLab/XiYanSQL-QwenCoder-7B-2502
-pipeline_tag: text-generation
+- XGenerationLab/XiYanSQL-QwenCoder-32B-2412
+base_model_relation: adapter
 language:
 - en
 - zh
+license: apache-2.0
 ---
 
-
-We are excited to update our new XiYanSQL-QwenCoder series models. This version includes significant optimizations over the previous one, achieving new SOTA performance for single models.
-
 ### 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)
+🤖[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.
+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.
+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
 
@@ -42,23 +43,24 @@ In this evaluation, we have also added a real-world SQL benchmark (the DW test s
 ## 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-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%      |
-| 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%      |
+| 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-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-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%       |
+
 
 
 
@@ -71,6 +73,7 @@ Currently, we mainly support mainstream dialects like SQLite, PostgreSQL, and My
 - 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】。
@@ -95,7 +98,7 @@ nl2sqlite_template_cn = """你是一名{dialect}专家，现在需要阅读并
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "XGenerationLab/XiYanSQL-QwenCoder-32B-2502"
+model_name = "XGenerationLab/XiYanSQL-QwenCoder-32B-2504"
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     torch_dtype=torch.bfloat16,
@@ -134,7 +137,7 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```python
 from vllm import LLM, SamplingParams
 from transformers import AutoTokenizer
-model_path = "XGenerationLab/XiYanSQL-QwenCoder-32B-2502"
+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(