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docs/stable/cli/cli_api.md

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@@ -43,7 +43,7 @@ After setting up the ServerlessLLM cluster, you can use the commands listed belo
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```
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### sllm-cli deploy
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Deploy a model using a configuration file or model name, with options to overwrite default configurations. The configuration file requires minimal specifications, as sensible defaults are provided for advanced configuration options.
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Deploy a model using a configuration file or model name, with options to overwrite default configurations. The configuration file requires minimal specifications, as sensible defaults are provided for advanced configuration options.
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For more details on the advanced configuration options and their default values, please refer to the [Example Configuration File](#example-configuration-file-configjson) section.
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docs/stable/getting_started/docker_quickstart.md

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## Run ServerlessLLM using Docker
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First, let's start a local Docker-based ray cluster to run ServerlessLLM.
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First, let's start a local Docker-based ray cluster to run ServerlessLLM.
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### Step 1: Build Docker Images
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docs/stable/getting_started/multi_machine_setup.md

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#### Configure the Environment
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**On any machine, open a new terminal, activate the `sllm` environment, and set the `LLM_SERVER_URL` environment variable:**
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> Replace `<HEAD_NODE_IP>` with the actual IP address of the head node.
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> Replace `<HEAD_NODE_IP>` with the actual IP address of the head node.
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```bash
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conda activate sllm
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**Make sure the model is successfully deployed before querying.**
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> Replace `<HEAD_NODE_IP>` with the actual IP address of the head node.
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> Replace `<HEAD_NODE_IP>` with the actual IP address of the head node.
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```bash
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curl http://<HEAD_NODE_IP>:8343/v1/chat/completions \

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