Update README.md

README.md

@@ -139,26 +139,26 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
 **Deployment**
 
-For deployment, you can use `sglang>=0.5.2` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
+For deployment, you can use `sglang>=0.5.2` or `vllm>=0.10.2` or to create an OpenAI-compatible API endpoint:
 - SGLang:
     ```shell
-    python -m sglang.launch_server --model-path tablegpt/TableGPT-R1 --served-model-name TableGPT-R1 --reasoning-parser qwen3
+    python -m sglang.launch_server --model-path tablegpt/TableGPT-R1 --port 8080 --served-model-name TableGPT-R1 --reasoning-parser qwen3
     ```
 - vLLM:
     ```shell
-    vllm serve tablegpt/TableGPT-R1 --served-model-name TableGPT-R1 --enable-reasoning --reasoning-parser deepseek_r1
+    vllm serve tablegpt/TableGPT-R1 --port 8080 --served-model-name TableGPT-R1 --reasoning-parser deepseek_r1
     ```
 
 Then you can access the Chat API by:
 
 ```bash
-curl http://localhost:xxx/v1/chat/completions \
+curl http://localhost:8080/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
     "model": "TableGPT-R1",
     "messages": [
         {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "xxxxx?"}
+        {"role": "user", "content": "Give me a short introduction to large language model."}
     ]
     }'