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For more detailed instructions, refer to [GuideLLM README](https://github.com/vllm-project/guidellm/blob/main/README.md).
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### 1.3 Verify Installation
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#### 1.3 Verify Installation
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```bash
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guidellm --help
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```
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### 1.4 Apply for account and api_key in D.run
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#### 1.4 Apply for Account and API Key in D.run
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Firstly, register an account, refer to [D.run Registration](https://docs.d.run/en/#register-account); then, create an API key, refer to [D.run API Key](https://docs.d.run/en/#register-account); finally, charge your account at [D.run Account Management](https://docs.d.run/en/#register-account).
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### 1.5 Chat with model in D.run
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#### 1.5 Chat with Model in D.run
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Check if you can use chat model in D.run
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Check if you can use the chat model in D.run.
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### 1.6 Find out the http request URL and body
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#### 1.6 Find Out the HTTP Request URL and Body
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Use Developer Tool in Chrome browser or F12 to open Network, then chat with LLM model to catch http request URL and body
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Use the Developer Tool in Chrome browser or press F12 to open Network, then chat with the LLM model to capture the HTTP request URL and body.
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In this request, the vllm backend service URL is `https://chat.d.run`; vllm model is `public/qwen2.5-72b-instruct-awq`. These two pieces of information will be used in the following benchmark command.
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### 1.7 Download one chat dataset from modelscope
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#### 1.7 Download a Chat Dataset from Modelscope
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Download the chat dataset JSON file `Open-Source-Meeseeks-high-quality.json` from [Modelscope - Meeseeks](https://modelscope.cn/datasets/meituan/Meeseeks/files).
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After benchmark completion, key results are clear and straight, e.g.:
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After the benchmark completes, key results are clear and straightforward, such as:
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