update vllm config

README.md

@@ -53,7 +53,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 Alternatively, you may serve the model using VLLM:
 
 ```
-vllm serve LLM360/K2-V2-Instruct --tensor-parallel-size 8 --port 8000 --revision "sft_final"
+vllm serve LLM360/K2-V2-Instruct --tensor-parallel-size 8 --port 8000
 ```
 
 K2-V2-Instruct uses `reasoning_effort="low"|"medium"|"high"` in the chat template to determine reasoning effort. If you cannot use `tokenizer.apply_chat_template`, you may also pass in these arguments using `extra_body` and `chat_template_kwargs`: