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- Server Arguments - vLLM
To see the available CLI arguments, run vllm serve --help! You can load CLI arguments via a YAML config file The argument names must be the long form of those outlined above For example: To use the above config file:
- [Bug]: vllm serve --config. yaml - Order of arguments matters? - GitHub
When serving a vllm server with vllm serve path to model --config path to config yaml the position of the argument served-model-name seems to be cruical to successfully run the server P s Why is collect_env py showing vLLM Version: 0 6 1 dev238+ge2c6e0a82
- docs. vllm. ai
The order of priorities is `command line > config file values > defaults` e g `vllm serve SOME_MODEL --config config yaml`, SOME_MODEL takes precedence over `model` in config file
- 服务器参数 - vLLM 文档
vllm serve 命令用于启动 OpenAI 兼容服务器。 要查看可用的 CLI 参数，请运行 vllm serve --help! 您可以通过 YAML 配置文件加载 CLI 参数。 参数名称必须是 上方 概述的长格式名称。 例如 使用上述配置文件 命令行 > 配置文件值 > 默认值 vllm serve SOME_MODEL --config config yaml model
- Serve with vLLM | Kithara documentation
In order to serve your finetuned model, first make sure that your output checkpoints are exported to a GCS bucket or persistent volume You can do this byoverriding the model_output_dir parameter in the config yaml:
- Tutorial: Basic vLLM Configurations | production-stack
In this tutorial, you configured and deployed a vLLM serving engine with GPU support in a Kubernetes environment You also learned how to verify its deployment and ensure it is running as expected For further customization, refer to the values yaml file and Helm chart documentation
- VLLM | liteLLM
Usage - LiteLLM Proxy Server (calling OpenAI compatible endpoint) Here's how to call an OpenAI-Compatible Endpoint with the LiteLLM Proxy Server Modify the config yaml
- vllm serve的参数大全及其解释 - CSDN博客
本文介绍了如何使用 vllm 库在单机单卡环境下部署和运行模型。通过命令行工具 vllm serve，用户可以指定模型名称或本地路径，并设置相关参数来启动服务。默认情况下，模型会从 Hugging Face 下载，数据类型为 auto。
- vLLM | Continue
Run their OpenAI-compatible server using vllm serve See their server documentation and the engine arguments documentation We recommend configuring Llama3 1 8B as your chat model We recommend configuring Qwen2 5-Coder 1 5B as your autocomplete model We recommend configuring Nomic Embed Text as your embeddings model
- Serving LLMs — Ray 2. 47. 1
This command generates two files: an LLM config file, saved in model_config , and a Ray Serve config file, serve_TIMESTAMP yaml, that you can reference and re-run in the future After reading and reviewing the generated model config, see the vLLM engine configuration docs for further customization
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