# OpenLLM **Repository Path**: monkeycc/OpenLLM ## Basic Information - **Project Name**: OpenLLM - **Description**: OpenLLM是一个开源平台，旨在促进大型语言模型（LLM）在实际应用程序中的部署和操作。使用 OpenLLM，您可以在任何开源 LLM 上运行推理，将它们部署在云或本地，并构建强大的 AI 应用程序。 - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 0 - **Created**: 2023-10-26 - **Last Updated**: 2024-09-14 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 🦾 OpenLLM: Self-Hosting LLMs Made Easy [![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) [![CI](https://results.pre-commit.ci/badge/github/bentoml/OpenLLM/main.svg)](https://results.pre-commit.ci/latest/github/bentoml/OpenLLM/main) [![X](https://badgen.net/badge/icon/@bentomlai/000000?icon=twitter&label=Follow)](https://twitter.com/bentomlai) [![Community](https://badgen.net/badge/icon/Community/562f5d?icon=slack&label=Join)](https://l.bentoml.com/join-slack) OpenLLM allows developers to run **any open-source LLMs** (Llama 3.1, Qwen2, 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). Understand the [design philosophy of OpenLLM](https://www.bentoml.com/blog/from-ollama-to-openllm-running-llms-in-the-cloud). ## Get Started Run the following commands to install OpenLLM and explore it interactively. ```bash pip install openllm # or pip3 install openllm openllm hello ``` ![hello](https://github.com/user-attachments/assets/5af19f23-1b34-4c45-b1e0-a6798b4586d1) ## Supported models 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. | Model | Parameters | Quantization | Required GPU | Start a Server | | --------- | ---------- | ------------ | ------------- | --------------------------------- | | Llama 3.1 | 8B | - | 24G | `openllm serve llama3.1:8b` | | Llama 3.1 | 8B | AWQ 4bit | 12G | `openllm serve llama3.1:8b-4bit` | | Llama 3.1 | 70B | AWQ 4bit | 80G | `openllm serve llama3.1:70b-4bit` | | Llama 2 | 7B | - | 16G | `openllm serve llama2:7b` | | Llama 2 | 7B | AWQ 4bit | 12G | `openllm serve llama2:7b-4bit` | | Mistral | 7B | - | 24G | `openllm serve mistral:7b` | | Qwen2 | 1.5B | - | 12G | `openllm serve qwen2:1.5b` | | Gemma | 7B | - | 24G | `openllm serve gemma:7b` | | Phi3 | 3.8B | - | 12G | `openllm serve phi3:3.8b` | ... For the full model list, see the [OpenLLM models repository](https://github.com/bentoml/openllm-models). ## Start an LLM server To start an LLM server locally, use the `openllm serve` command and specify the model version. ```bash openllm serve llama3:8b ``` 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: - **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. Here are some examples:
OpenAI Python client ```python from openai import OpenAI client = OpenAI(base_url='http://localhost:3000/v1', api_key='na') # Use the following func to get the available models # model_list = client.models.list() # print(model_list) chat_completion = client.chat.completions.create( model="meta-llama/Meta-Llama-3-8B-Instruct", messages=[ { "role": "user", "content": "Explain superconductors like I'm five years old" } ], stream=True, ) for chunk in chat_completion: print(chunk.choices[0].delta.content or "", end="") ```
LlamaIndex ```python from llama_index.llms.openai import OpenAI llm = OpenAI(api_bese="http://localhost:3000/v1", model="meta-llama/Meta-Llama-3-8B-Instruct", api_key="dummy") ... ```
## Chat UI OpenLLM provides a chat UI at the `/chat` endpoint for the launched LLM server at http://localhost:3000/chat. openllm_ui ## Chat with a model in the CLI To start a chat conversation in the CLI, use the `openllm run` command and specify the model version. ```bash openllm run llama3:8b ``` ## Model repository 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: ```bash openllm model list ``` To ensure your local list of models is synchronized with the latest updates from all connected repositories, run: ```bash openllm repo update ``` To review a model’s information, run: ```bash openllm model get llama3:8b ``` ### Add a model to the default model repository 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). ### Set up a custom repository 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. 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. Then, register your custom model repository with OpenLLM: ```bash openllm repo add ``` **Note**: Currently, OpenLLM only supports adding public repositories. ## Deploy to 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. [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: ```bash openllm deploy llama3:8b ``` Once the deployment is complete, you can run model inference on the BentoCloud console: bentocloud_ui ## Community 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) ## Contributing 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: - 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. ## Acknowledgements This project uses the following open-source projects: - [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 - [chujiezheng/chat_templates](https://github.com/chujiezheng/chat_templates) - [astral-sh/uv](https://github.com/astral-sh/uv) for blazing fast model requirements installing We are grateful to the developers and contributors of these projects for their hard work and dedication.