add vllm implement in README

README_zh.md

@@ -37,6 +37,46 @@ with torch.no_grad():
     outputs = outputs[:, inputs['input_ids'].shape[1]:]
     print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
+使用 `vllm==0.5.1` 快速启动 [codegeex4-all-9b](https://huggingface.co/THUDM/codegeex4-all-9b)：
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+# CodeGeeX4-ALL-9B
+# max_model_len, tp_size = 1048576, 4
+# 如果出现内存不足（OOM），减少max_model_len，或增加tp_size
+max_model_len, tp_size = 131072, 1
+model_name = "codegeex4-all-9b"
+prompt = [{"role": "user", "content": "Hello"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+llm = LLM(
+    model=model_name,
+    tensor_parallel_size=tp_size,
+    max_model_len=max_model_len,
+    trust_remote_code=True,
+    enforce_eager=True,
+    # 如果出现OOM，尝试使用以下参数
+    # enable_chunked_prefill=True,
+    # max_num_batched_tokens=8192
+)
+stop_token_ids = [151329, 151336, 151338]
+sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids)
+
+inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
+outputs = llm.generate(prompts=inputs, sampling_params=sampling_params)
+
+print(outputs[0].outputs[0].text)
+```
+
+通过 vllm 设置 OpenAI 兼容服务，详细信息请查看 [OpenAI 兼容服务器](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html)：
+
+```bash
+python -m vllm.entrypoints.openai.api_server \
+     --model THUDM/codegeex4-all-9b \
+     --trust_remote_code
+```
 
 ## 用户指南
 我们为 CodeGeeX4-ALL-9B 提供了用户指南，帮助用户快速了解和使用该模型：