# OpenLLM — Serve Open-Source LLMs

> Serve open-source LLMs with a unified CLI, multiple backends, and production deployment paths. Start with `openllm hello`, then serve a real model.

## Install

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# OpenLLM — Serve Open-Source LLMs

> Serve open-source LLMs with a unified CLI, multiple backends, and production deployment paths. Start with `openllm hello`, then serve a real model.

## Quick Use

1. Install:
   ```bash
   pip install openllm
   ```
2. Run:
   ```bash
   openllm hello
   ```
3. Verify:
   - Run one `openllm serve ...` command for a small model and confirm you can hit the HTTP endpoint locally.


---

## Intro

Serve open-source LLMs with a unified CLI, multiple backends, and production deployment paths. Start with `openllm hello`, then serve a real model.

- **Best for:** Teams who want a consistent local-to-cloud path for serving open models without hand-rolling inference servers
- **Works with:** Python, CLI workflows, open model serving (local + container/cloud patterns per repo docs)
- **Setup time:** 20 minutes


### Quantitative Notes

- Setup time ~20 minutes (pip install + hello + first serve)
- GitHub stars + forks (verified): see Source & Thanks
- Start with a small model first, then scale to larger sizes to avoid long downloads


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## Practical Notes

A pragmatic workflow is: validate the runtime with `openllm hello`, then serve a small model locally, write a single health-check endpoint, and finally containerize. Track cold start time and memory usage, and bake model downloads into images only when you accept the tradeoff.

**Safety note:** Do not expose unauthenticated model endpoints on the public internet; add auth, rate limits, and logging.

### FAQ

**Q: Is OpenLLM an inference engine?**
A: It’s a serving toolkit/CLI that helps you run models using supported backends and deploy patterns.

**Q: Can I use it in Docker/Kubernetes?**
A: Yes. The repo describes container and cloud deployment workflows; start local first.

**Q: How do I pick a model?**
A: Pick the smallest model that meets quality requirements; measure latency and memory before scaling up.

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## Source & Thanks

> GitHub: https://github.com/bentoml/OpenLLM
> Owner avatar: https://avatars.githubusercontent.com/u/49176046?v=4
> License (SPDX): Apache-2.0
> GitHub stars (verified via `api.github.com/repos/bentoml/OpenLLM`): 12,318
> GitHub forks (verified via `api.github.com/repos/bentoml/OpenLLM`): 810


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# OpenLLM——用统一 CLI 部署开源大模型

> 用统一 CLI 部署开源 LLM：支持多种推理后端与更贴近生产的部署路径（本地、容器与云）。先跑 `openllm hello`，再切到真实模型做服务化、健康检查与接口验证，并便于统一管理版本。

## 快速使用

1. 安装：
   ```bash
   pip install openllm
   ```
2. 运行：
   ```bash
   openllm hello
   ```
3. 验证：
   - Run one `openllm serve ...` command for a small model and confirm you can hit the HTTP endpoint locally.


---

## 简介

用统一 CLI 部署开源 LLM：支持多种推理后端与更贴近生产的部署路径（本地、容器与云）。先跑 `openllm hello`，再切到真实模型做服务化、健康检查与接口验证，并便于统一管理版本。

- **适合谁（Best for）:** 希望从本地到云端用一致方式部署开源模型、又不想手写推理服务的团队
- **兼容工具（Works with）:** Python、CLI 工作流、开源模型服务化（本地 + 容器/云部署方式见仓库）
- **安装时间（Setup time）:** 20 分钟


### 量化信息

- 跑通约 20 分钟（pip 安装 + hello + 第一次 serve）
- GitHub stars + forks（已核验）：见「来源与感谢」
- 建议先用小模型跑通，再逐步升级更大模型，避免下载/启动时间过长


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## 实战要点

务实的流程：先用 `openllm hello` 验证运行时，再本地 serve 一个小模型，补一个健康检查接口，最后再容器化。重点关注冷启动时间与内存占用；只有在接受镜像体积换取启动速度时，才把模型下载打进镜像。

**安全提示：** 不要把未鉴权的模型接口直接暴露公网；需要配鉴权、限流与日志审计。

### FAQ

**Q: OpenLLM 是推理引擎吗？**
A: 它更像服务化工具链/CLI：封装后端与部署流程，帮你把模型跑起来并暴露接口。

**Q: 能用于 Docker/K8s 吗？**
A: 可以。仓库提供容器与云部署流程；建议先本地跑通再上云。

**Q: 模型怎么选？**
A: 优先选择满足质量要求的最小模型，并先测延迟与内存再扩大规模。

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## 来源与感谢

> GitHub：https://github.com/bentoml/OpenLLM
> Owner avatar：https://avatars.githubusercontent.com/u/49176046?v=4
> 许可证（SPDX）：Apache-2.0
> GitHub stars（已通过 `api.github.com/repos/bentoml/OpenLLM` 核验）：12,318
> GitHub forks（已通过 `api.github.com/repos/bentoml/OpenLLM` 核验）：810


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Source: https://tokrepo.com/en/workflows/openllm-serve-open-source-llms
Author: AI Open Source