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[Doc] update(example model): for OpenAI compatible serving (#4503)
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@ -4,7 +4,7 @@ vLLM provides an HTTP server that implements OpenAI's [Completions](https://plat
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You can start the server using Python, or using [Docker](deploying_with_docker.rst):
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```bash
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python -m vllm.entrypoints.openai.api_server --model mistralai/Mistral-7B-Instruct-v0.2 --dtype auto --api-key token-abc123
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python -m vllm.entrypoints.openai.api_server --model NousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key token-abc123
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```
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To call the server, you can use the official OpenAI Python client library, or any other HTTP client.
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@ -16,7 +16,7 @@ client = OpenAI(
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)
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completion = client.chat.completions.create(
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model="mistralai/Mistral-7B-Instruct-v0.2",
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model="NousResearch/Meta-Llama-3-8B-Instruct",
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messages=[
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{"role": "user", "content": "Hello!"}
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]
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@ -37,7 +37,7 @@ Or directly merge them into the JSON payload if you are using HTTP call directly
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```python
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completion = client.chat.completions.create(
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model="mistralai/Mistral-7B-Instruct-v0.2",
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model="NousResearch/Meta-Llama-3-8B-Instruct",
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messages=[
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{"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"}
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],
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@ -87,7 +87,7 @@ In order for the language model to support chat protocol, vLLM requires the mode
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a chat template in its tokenizer configuration. The chat template is a Jinja2 template that
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specifies how are roles, messages, and other chat-specific tokens are encoded in the input.
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An example chat template for `mistralai/Mistral-7B-Instruct-v0.2` can be found [here](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#instruction-format)
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An example chat template for `NousResearch/Meta-Llama-3-8B-Instruct` can be found [here](https://github.com/meta-llama/llama3?tab=readme-ov-file#instruction-tuned-models)
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Some models do not provide a chat template even though they are instruction/chat fine-tuned. For those model,
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you can manually specify their chat template in the `--chat-template` parameter with the file path to the chat
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