Update README.md

README.md

@@ -62,7 +62,7 @@ transformers >= 4.37.0
 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.
 
-```
+```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}
@@ -108,6 +108,36 @@ 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-2412"
+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
+```
+
+
+
+
 ## 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!
 ```bibtex