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docs/stable/store/quickstart.md

@@ -109,7 +109,14 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ## Usage with vLLM
 
-To use ServerlessLLM as a load format for vLLM, you need to apply our patch `serverless_llm/store/vllm_patch/sllm_load.patch` to the installed vLLM library. Therefore, please make sure you have read and followed the steps in the `vLLM Patch` section under our [installation guide](../getting_started/installation.md).
+:::tip
+To use ServerlessLLM as the load format for vLLM, you need to apply our patch `serverless_llm/store/vllm_patch/sllm_load.patch` to the installed vLLM library. Therefore, please ensure you have applied our `vLLM Patch` as instructed in [installation guide](../getting_started/installation.md).
+```bash
+VLLM_PATH=$(python -c "import vllm; import os; print(os.path.dirname(os.path.abspath(vllm.__file__)))")
+patch -p2 -d $VLLM_PATH < serverless_llm/store/vllm_patch/sllm_load.patch
+```
+:::
+
 
 Our api aims to be compatible with the `sharded_state` load format in vLLM. Thus, due to the model modifications about the model architecture done by vLLM, the model format for vLLM is **not** the same as we used in transformers. Thus, the `ServerlessLLM format` mentioned in the subsequent sections means the format integrated with vLLM, which is different from the `ServerlessLLM format` used in the previous sections.