docs: make Chinese README the default

2026-07-13 10:47:48 +00:00
parent 6aed39dfa7
commit 415bbf96dd
1 changed files with 65 additions and 59 deletions
@@ -1,6 +1,12 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/bentoml/OpenLLM) · [上游 README](https://github.com/bentoml/OpenLLM/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <div align="center">

-<h1>🦾 OpenLLM: Self-Hosting LLMs Made Easy</h1>
+<h1>🦾 OpenLLM：轻松自托管 LLM</h1>

 [![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202-green.svg)](https://github.com/bentoml/OpenLLM/blob/main/LICENSE)
 [![Releases](https://img.shields.io/pypi/v/openllm.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/openllm)
@@ -10,13 +16,13 @@

 </div>

-OpenLLM allows developers to run **any open-source LLMs** (Llama 3.3, Qwen2.5, Phi3 and [more](#supported-models)) or **custom models** as **OpenAI-compatible APIs** with a single command. It features a [built-in chat UI](#chat-ui), state-of-the-art inference backends, and a simplified workflow for creating enterprise-grade cloud deployment with Docker, Kubernetes, and [BentoCloud](#deploy-to-bentocloud).
+OpenLLM 让开发者只需一条命令，即可将**任意开源 LLM**（Llama 3.3、Qwen2.5、Phi3 及[更多](#supported-models)）或**自定义模型**作为**兼容 OpenAI 的 API** 运行。它提供[内置聊天 UI](#chat-ui)、最先进的推理后端，以及通过 Docker、Kubernetes 和 [BentoCloud](#deploy-to-bentocloud) 创建企业级云部署的简化工作流。

-Understand the [design philosophy of OpenLLM](https://www.bentoml.com/blog/from-ollama-to-openllm-running-llms-in-the-cloud).
+了解 [OpenLLM 的设计理念](https://www.bentoml.com/blog/from-ollama-to-openllm-running-llms-in-the-cloud).

-## Get Started
+## 快速开始

-Run the following commands to install OpenLLM and explore it interactively.
+运行以下命令安装 OpenLLM 并交互式探索。

 ```bash
 pip install openllm  # or pip3 install openllm
@@ -25,16 +31,16 @@ openllm hello

 ![hello](https://github.com/user-attachments/assets/5af19f23-1b34-4c45-b1e0-a6798b4586d1)

-## Supported models
+## 支持的模型

-OpenLLM supports a wide range of state-of-the-art open-source LLMs. You can also add a [model repository to run custom models](#set-up-a-custom-repository) with OpenLLM.
+OpenLLM 支持广泛的最先进开源 LLM。你也可以添加[模型仓库以运行自定义模型](#set-up-a-custom-repository) 并在 OpenLLM 中使用。

 <table>
  <tr>
-    <th>Model</th>
-    <th>Parameters</th>
-    <th>Required GPU</th>
-    <th>Start a Server</th>
+    <th>模型</th>
+    <th>参数量</th>
+    <th>所需 GPU</th>
+    <th>启动服务</th>
  </tr>
  <tr>
    <td>deepseek</td>
@@ -128,18 +134,18 @@ OpenLLM supports a wide range of state-of-the-art open-source LLMs. You can also
  </tr>
 </table>

-For the full model list, see the [OpenLLM models repository](https://github.com/bentoml/openllm-models).
+完整模型列表请参阅 [OpenLLM 模型仓库](https://github.com/bentoml/openllm-models).

-## Start an LLM server
+## 启动 LLM 服务

-To start an LLM server locally, use the `openllm serve` command and specify the model version.
+要在本地启动 LLM 服务，请使用 `openllm serve` 命令并指定模型版本。

 > [!NOTE]
-> OpenLLM does not store model weights. A Hugging Face token (HF_TOKEN) is required for gated models.
+> OpenLLM 不存储模型权重。访问受限（gated）模型需要 Hugging Face 令牌（HF_TOKEN）。
 >
-> 1. Create your Hugging Face token [here](https://huggingface.co/settings/tokens).
-> 2. Request access to the gated model, such as [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
-> 3. Set your token as an environment variable by running:
+> 1. 在[此处](https://huggingface.co/settings/tokens). 创建你的 Hugging Face 令牌
+> 2. 申请受限模型的访问权限，例如 [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
+> 3. 通过运行以下命令将令牌设置为环境变量：
 >    ```bash
 >    export HF_TOKEN=<your token>
 >    ```
@@ -148,17 +154,17 @@ To start an LLM server locally, use the `openllm serve` command and specify the
 openllm serve llama3.2:1b
 ```

-The server will be accessible at [http://localhost:3000](http://localhost:3000/), providing OpenAI-compatible APIs for interaction. You can call the endpoints with different frameworks and tools that support OpenAI-compatible APIs. Typically, you may need to specify the following:
+服务将在 [http://localhost:3000](http://localhost:3000/), 处可用，提供兼容 OpenAI 的 API 用于交互。你可以使用支持兼容 OpenAI API 的不同框架和工具调用端点。通常，你可能需要指定以下内容：

- **The API host address**: By default, the LLM is hosted at [http://localhost:3000](http://localhost:3000/).
- **The model name:** The name can be different depending on the tool you use.
- **The API key**: The API key used for client authentication. This is optional.
+- **API 主机地址**：默认情况下，LLM 托管于 [http://localhost:3000](http://localhost:3000/).
+- **模型名称：** 名称可能因你使用的工具而异。
+- **API 密钥**：用于客户端身份验证的 API 密钥。此项为可选。

-Here are some examples:
+以下是一些示例：

 <details>

-<summary>OpenAI Python client</summary>
+<summary>OpenAI Python 客户端</summary>

 ```python
 from openai import OpenAI
@@ -198,95 +204,95 @@ llm = OpenAI(api_bese="http://localhost:3000/v1", model="meta-llama/Llama-3.2-1B

 </details>

-## Chat UI
+## 聊天 UI

-OpenLLM provides a chat UI at the `/chat` endpoint for the launched LLM server at http://localhost:3000/chat.
+OpenLLM 在 `/chat` 端点为 http://localhost:3000/chat. 上启动的 LLM 服务提供聊天 UI。

 <img width="800" alt="openllm_ui" src="https://github.com/bentoml/OpenLLM/assets/5886138/8b426b2b-67da-4545-8b09-2dc96ff8a707">

-## Chat with a model in the CLI
+## 在 CLI 中与模型对话

-To start a chat conversation in the CLI, use the `openllm run` command and specify the model version.
+要在 CLI 中开始聊天对话，请使用 `openllm run` 命令并指定模型版本。

 ```bash
 openllm run llama3:8b
 ```

-## Model repository
+## 模型仓库

-A model repository in OpenLLM represents a catalog of available LLMs that you can run. OpenLLM provides a default model repository that includes the latest open-source LLMs like Llama 3, Mistral, and Qwen2, hosted at [this GitHub repository](https://github.com/bentoml/openllm-models). To see all available models from the default and any added repository, use:
+OpenLLM 中的模型仓库表示你可运行的可用 LLM 目录。OpenLLM 提供默认模型仓库，包含 Llama 3、Mistral 和 Qwen2 等最新开源 LLM，托管于[此 GitHub 仓库](https://github.com/bentoml/openllm-models). 要查看默认仓库及任何已添加仓库中的所有可用模型，请运行：

 ```bash
 openllm model list
 ```

-To ensure your local list of models is synchronized with the latest updates from all connected repositories, run:
+为确保本地模型列表与所有已连接仓库的最新更新同步，请运行：

 ```bash
 openllm repo update
 ```

-To review a model’s information, run:
+要查看模型信息，请运行：

 ```bash
 openllm model get llama3.2:1b
 ```

-### Add a model to the default model repository
+### 向默认模型仓库添加模型

-You can contribute to the default model repository by adding new models that others can use. This involves creating and submitting a Bento of the LLM. For more information, check out this [example pull request](https://github.com/bentoml/openllm-models/pull/1).
+你可以通过添加他人可使用的新模型来为默认模型仓库做贡献。这涉及创建并提交 LLM 的 Bento。更多信息请参阅此[示例拉取请求](https://github.com/bentoml/openllm-models/pull/1).

-### Set up a custom repository
+### 设置自定义仓库

-You can add your own repository to OpenLLM with custom models. To do so, follow the format in the default OpenLLM model repository with a `bentos` directory to store custom LLMs. You need to [build your Bentos with BentoML](https://docs.bentoml.com/en/latest/guides/build-options.html) and submit them to your model repository.
+你可以将包含自定义模型的自有仓库添加到 OpenLLM。为此，请遵循默认 OpenLLM 模型仓库的格式，使用 `bentos` 目录存储自定义 LLM。你需要[使用 BentoML 构建 Bentos](https://docs.bentoml.com/en/latest/guides/build-options.html) 并将其提交到你的模型仓库。

-First, prepare your custom models in a `bentos` directory following the guidelines provided by [BentoML to build Bentos](https://docs.bentoml.com/en/latest/guides/build-options.html). Check out the [default model repository](https://github.com/bentoml/openllm-repo) for an example and read the [Developer Guide](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md) for details.
+首先，按照 [BentoML 构建 Bentos 指南](https://docs.bentoml.com/en/latest/guides/build-options.html). 在 `bentos` 目录中准备自定义模型。可参考[默认模型仓库](https://github.com/bentoml/openllm-repo) 示例，并阅读[开发者指南](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md) 了解详情。

-Then, register your custom model repository with OpenLLM:
+接下来，在 OpenLLM 中注册你的自定义模型仓库：

 ```bash
 openllm repo add <repo-name> <repo-url>
 ```

-**Note**: Currently, OpenLLM only supports adding public repositories.
+**注意**：目前，OpenLLM 仅支持添加公开仓库。

-## Deploy to BentoCloud
+## 部署到 BentoCloud

-OpenLLM supports LLM cloud deployment via BentoML, the unified model serving framework, and BentoCloud, an AI inference platform for enterprise AI teams. BentoCloud provides fully-managed infrastructure optimized for LLM inference with autoscaling, model orchestration, observability, and many more, allowing you to run any AI model in the cloud.
+OpenLLM 支持通过 BentoML（统一模型服务框架）和 BentoCloud（面向企业 AI 团队的 AI 推理平台）进行 LLM 云端部署。BentoCloud 提供针对 LLM 推理优化的全托管基础设施，具备自动扩缩容、模型编排、可观测性等众多功能，让你能够在云端运行任意 AI 模型。

-[Sign up for BentoCloud](https://www.bentoml.com/) for free and [log in](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html). Then, run `openllm deploy` to deploy a model to BentoCloud:
+[注册 BentoCloud](https://www.bentoml.com/) 免费账号，并[登录](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html). 然后运行 `openllm deploy` 将模型部署到 BentoCloud：

 ```bash
 openllm deploy llama3.2:1b --env HF_TOKEN
 ```

 > [!NOTE]
-> If you are deploying a gated model, make sure to set HF_TOKEN in enviroment variables.
+> 如果你正在部署受限（gated）模型，请确保在环境变量中设置 HF_TOKEN。

-Once the deployment is complete, you can run model inference on the BentoCloud console:
+部署完成后，你可以在 BentoCloud 控制台运行模型推理：

 <img width="800" alt="bentocloud_ui" src="https://github.com/bentoml/OpenLLM/assets/65327072/4f7819d9-73ea-488a-a66c-f724e5d063e6">

-## Community
+## 社区

-OpenLLM is actively maintained by the BentoML team. Feel free to reach out and join us in our pursuit to make LLMs more accessible and easy to use 👉 [Join our Slack community!](https://l.bentoml.com/join-slack)
+OpenLLM 由 BentoML 团队积极维护。欢迎与我们联系，加入我们一起让 LLM 更易获取、更易使用的努力 👉 [加入我们的 Slack 社区！](https://l.bentoml.com/join-slack)

-## Contributing
+## 贡献

-As an open-source project, we welcome contributions of all kinds, such as new features, bug fixes, and documentation. Here are some of the ways to contribute:
+作为开源项目，我们欢迎各类贡献，例如新功能、缺陷修复和文档改进。以下是一些贡献方式：

- Repost a bug by [creating a GitHub issue](https://github.com/bentoml/OpenLLM/issues/new/choose).
- [Submit a pull request](https://github.com/bentoml/OpenLLM/compare) or help review other developers’ [pull requests](https://github.com/bentoml/OpenLLM/pulls).
- Add an LLM to the OpenLLM default model repository so that other users can run your model. See the [pull request template](https://github.com/bentoml/openllm-models/pull/1).
- Check out the [Developer Guide](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md) to learn more.
+- 通过[创建 GitHub issue](https://github.com/bentoml/OpenLLM/issues/new/choose). 报告缺陷
+- [提交 pull request](https://github.com/bentoml/OpenLLM/compare)，或帮助审查其他开发者的 [pull request](https://github.com/bentoml/OpenLLM/pulls).
+- 将 LLM 添加到 OpenLLM 默认模型仓库，以便其他用户运行你的模型。请参阅 [pull request 模板](https://github.com/bentoml/openllm-models/pull/1).
+- 查看[开发者指南](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md) 了解更多。

-## Acknowledgements
+## 致谢

-This project uses the following open-source projects:
+本项目使用了以下开源项目：

- [bentoml/bentoml](https://github.com/bentoml/bentoml) for production level model serving
- [vllm-project/vllm](https://github.com/vllm-project/vllm) for production level LLM backend
- [blrchen/chatgpt-lite](https://github.com/blrchen/chatgpt-lite) for a fancy Web Chat UI
- [astral-sh/uv](https://github.com/astral-sh/uv) for blazing fast model requirements installing
+- [bentoml/bentoml](https://github.com/bentoml/bentoml) 用于生产级模型服务
+- [vllm-project/vllm](https://github.com/vllm-project/vllm) 用于生产级 LLM 后端
+- [blrchen/chatgpt-lite](https://github.com/blrchen/chatgpt-lite) 用于精美的 Web 聊天 UI
+- [astral-sh/uv](https://github.com/astral-sh/uv) 用于极速安装模型依赖

-We are grateful to the developers and contributors of these projects for their hard work and dedication.
+我们感谢这些项目的开发者和贡献者所付出的辛勤工作与奉献。