Quickstart - vLLMdocs.vllm.ai/en/latest/getting_started/quickstart.html#openai-compatible-server 以Qwen1.5-14b-chat模型为例,假设是单机四卡,要使用 --tensor-parallel-size 参数,防止只用一个卡导致OOM: python -m vllm.entrypoints.openai.api_server --model /model_path/Qwen1.5-14B-Chat --tenso...
8 changes: 7 additions & 1 deletion 8 docs/source/serving/openai_compatible_server.md Original file line numberDiff line numberDiff line change @@ -112,7 +112,13 @@ completion = client.chat.completions.create( ## Extra HTTP Headers Only `X-Request-Id` HTTP request header is supporte...
Using universal request ID/UUID when logging is common practice for production systems involving multiple components. My team wanted to use an UUID from upstream to trace logs produced by vLLM's OpenAI compatible webserver, but it doesn't seem like this is supported. Currently, vLLM generates ...
可以看到LM Studio本地推理服务器提供的所有终结点。这些终结点参考了OpenAI的终结点,代表服务器可以提供的不同功能或服务: [2024-01-07 17:14:44.375] [INFO] [LM STUDIO SERVER] Verbose server logs are ENABLED [2024-01-07 17:14:44.379] [INFO] [LM STUDIO SERVER] Success! HTTP server listening on...
Medium:Running a Local OpenAI-Compatible Mixtral Server with LM Studio LM Studio是一款易于使用的桌面应用程序,用于部署开源的本地大型语言模型。本文中,将介绍使用LM Studio设置与OpenAI兼容的本地服务器的简单步骤。可以通过更改基础URL,将完成请求指向本地Mixtral而不是OpenAI服务器,从而将OpenAI客户端代码无缝转...
docker run -it --net=host --gpus all --rm \ -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \ -e HF_TOKEN \ nvcr.io/nvidia/tritonserver:25.01-vllm-python-py3 Launch the OpenAI-compatible Triton Inference Server: cd /opt/tritonserver/python/openai # NOTE: Adjust the -...
Using Advanced Reasoning Model on EdgeAI Part 1 - Quantization, Conversion, Performance DeepSeek-R1 is very popular, and it can achieve the same capabilities as OpenAI o1 in advanced reasoning. Microsoft has also added DeepSeek-R1 models to Azure AI Foundry and GitHub Models. We can compare ...
fromopenaiimportOpenAI# init client and connect to localhost serverclient = OpenAI( api_key="fake-api-key", base_url="http://localhost:8000/v1/"# change the default port if needed) stream = client.chat.completions.create( model="mock-gpt-model", ...
OpenAI models can take some time to fully respond, so we’ll show you how to stream responses from functions using Server Sent Events (SSE).
from openai import OpenAI import os def get_response(): client = OpenAI( # 如果您没有配置环境变量,请用百炼API Key将下行替换为:api_key="sk-xxx" api_key=os.getenv("DASHSCOPE_API_KEY"), # 填写DashScope SDK的base_url base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", ) ...