<div align="center">
<img src="./assets/xorbits-logo.png" width="180px" alt="xorbits" />

# Xorbits Inference: Model Serving Made Easy 🤖

<p align="center">
  <a href="https://xinference.co">Xinference Enterprise</a> ·
  <a href="https://inference.readthedocs.io/en/latest/getting_started/installation.html#installation">Self-hosting</a> ·
  <a href="https://inference.readthedocs.io/">Documentation</a>
</p>

[![PyPI Latest Release](https://img.shields.io/pypi/v/xinference.svg?style=for-the-badge)](https://pypi.org/project/xinference/)
[![License](https://img.shields.io/pypi/l/xinference.svg?style=for-the-badge)](https://github.com/xorbitsai/inference/blob/main/LICENSE)
[![Build Status](https://img.shields.io/github/actions/workflow/status/xorbitsai/inference/python.yaml?branch=main&style=for-the-badge&label=GITHUB%20ACTIONS&logo=github)](https://actions-badge.atrox.dev/xorbitsai/inference/goto?ref=main)
[![Docker Pulls](https://img.shields.io/docker/pulls/xprobe/xinference?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xprobe/xinference)
[![Discord](https://img.shields.io/badge/join_Discord-5462eb.svg?logo=discord&style=for-the-badge&logoColor=%23f5f5f5)](https://discord.gg/Xw9tszSkr5)
[![Telegram](https://img.shields.io/badge/join_Telegram-26A5E4.svg?logo=telegram&style=for-the-badge&logoColor=white)](https://t.me/+nCNpwmySwk9iYmI1)
[![Twitter](https://img.shields.io/twitter/follow/xorbitsio?logo=x&style=for-the-badge)](https://twitter.com/xorbitsio)

<p align="center">
  <a href="./README.md"><img alt="English" src="https://img.shields.io/badge/English-454545?style=for-the-badge"></a>
  <a href="./READMES/README_ja_JP.md"><img alt="日本語" src="https://img.shields.io/badge/日本語-d9d9d9?style=for-the-badge"></a>
  <a href="./READMES/README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-d9d9d9?style=for-the-badge"></a>
  <a href="./READMES/README_de.md"><img alt="Deutsch" src="https://img.shields.io/badge/Deutsch-d9d9d9?style=for-the-badge"></a>
  <a href="./READMES/README_fr.md"><img alt="Français" src="https://img.shields.io/badge/Français-d9d9d9?style=for-the-badge"></a>
  <br>
  <a href="./READMES/README_es.md"><img alt="Español" src="https://img.shields.io/badge/Español-d9d9d9?style=for-the-badge"></a>
  <a href="./READMES/README_it.md"><img alt="Italiano" src="https://img.shields.io/badge/Italiano-d9d9d9?style=for-the-badge"></a>
  <a href="./READMES/README_pt_BR.md"><img alt="Português" src="https://img.shields.io/badge/Português-d9d9d9?style=for-the-badge"></a>
  <a href="./READMES/README_zh_TW.md"><img alt="繁體中文" src="https://img.shields.io/badge/繁體中文-d9d9d9?style=for-the-badge"></a>
  <a href="./READMES/README_zh_CN.md"><img alt="简体中文" src="https://img.shields.io/badge/简体中文-d9d9d9?style=for-the-badge"></a>
</p>
</div>
<br />


Xorbits Inference(Xinference) is a powerful and versatile library designed to serve language, 
speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy 
and serve your or state-of-the-art built-in models using just a single command. Whether you are a 
researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full 
potential of cutting-edge AI models.

<div align="center">
<i><a href="https://discord.gg/Xw9tszSkr5">👉 Join our Discord community!</a> · <a href="https://t.me/+nCNpwmySwk9iYmI1">Join our Telegram group!</a></i>
</div>

## 🔥 Hot Topics
### Framework Enhancements
- Xinference 3.0.0 is available with migration notes and breaking changes: [Release Notes](https://xinference.co/release_notes/v3.0.0.html)
- Agent-native Serving: Xinference integrates with [Xagent](https://github.com/xorbitsai/xagent) to enable dynamic planning, tool use, and autonomous multi-step reasoning — moving beyond static pipelines.
- Auto batch: Multiple concurrent requests are automatically batched, significantly improving throughput: [#4197](https://github.com/xorbitsai/inference/pull/4197)
- [Xllamacpp](https://github.com/xorbitsai/xllamacpp): New llama.cpp Python binding, maintained by Xinference team, supports continuous batching and is more production-ready.: [#2997](https://github.com/xorbitsai/inference/pull/2997)
- Distributed inference: running models across workers: [#2877](https://github.com/xorbitsai/inference/pull/2877)
- VLLM enhancement: Shared KV cache across multiple replicas: [#2732](https://github.com/xorbitsai/inference/pull/2732)
### New Models
- Built-in support for [MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3): [#5258](https://github.com/xorbitsai/inference/pull/5258)
- Built-in support for VibeThinker series ([1.5B](https://huggingface.co/WeiboAI/VibeThinker-1.5B), [3B](https://huggingface.co/WeiboAI/VibeThinker-3B)): [#5085](https://github.com/xorbitsai/inference/pull/5085)
- Built-in support for Nex-N2 series ([mini](https://huggingface.co/nex-agi/Nex-N2-mini), [Pro](https://huggingface.co/nex-agi/Nex-N2-Pro), [Pro-fp8](https://huggingface.co/nex-agi/Nex-N2-Pro-fp8)): [#5094](https://github.com/xorbitsai/inference/pull/5094)
- Built-in support for [Unlimited-OCR](https://huggingface.co/baidu/Unlimited-OCR): [#5103](https://github.com/xorbitsai/inference/pull/5103)
- Built-in support for [Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B): [#5119](https://github.com/xorbitsai/inference/pull/5119)
- Built-in support for [MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B): [#5010](https://github.com/xorbitsai/inference/pull/5010)
- Built-in support for jina-embeddings-v5 series ([text-nano](https://huggingface.co/jinaai/jina-embeddings-v5-text-nano), [text-small](https://huggingface.co/jinaai/jina-embeddings-v5-text-small), [omni-nano](https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano), [omni-small](https://huggingface.co/jinaai/jina-embeddings-v5-omni-small)): [#5018](https://github.com/xorbitsai/inference/pull/5018)
- Built-in support for MiniCPM-V-4.6 series ([MiniCPM-V-4.6](https://huggingface.co/openbmb/MiniCPM-V-4.6), [MiniCPM-V-4.6-Thinking](https://huggingface.co/openbmb/MiniCPM-V-4.6-Thinking)): [#5025](https://github.com/xorbitsai/inference/pull/5025)
### Integrations
- [Xagent](https://github.com/xorbitsai/xagent): an enterprise agent platform for building and running AI agents with planning, memory, and tool use — not limited to rigid workflows.
- [Dify](https://docs.dify.ai/advanced/model-configuration/xinference): an LLMOps platform that enables developers (and even non-developers) to quickly build useful applications based on large language models, ensuring they are visual, operable, and improvable.
- [FastGPT](https://github.com/labring/FastGPT): a knowledge-based platform built on the LLM, offers out-of-the-box data processing and model invocation capabilities, allows for workflow orchestration through Flow visualization.
- [RAGFlow](https://github.com/infiniflow/ragflow): is an open-source RAG engine based on deep document understanding.
- [MaxKB](https://github.com/1Panel-dev/MaxKB): MaxKB = Max Knowledge Brain, it is a powerful and easy-to-use AI assistant that integrates Retrieval-Augmented Generation (RAG) pipelines, supports robust workflows, and provides advanced MCP tool-use capabilities.


## Key Features
🌟 **Model Serving Made Easy**: Simplify the process of serving large language, speech 
recognition, and multimodal models. You can set up and deploy your models
for experimentation and production with a single command.

⚡️ **State-of-the-Art Models**: Experiment with cutting-edge built-in models using a single 
command. Inference provides access to state-of-the-art open-source models!

🖥 **Heterogeneous Hardware Utilization**: Make the most of your hardware resources with
[ggml](https://github.com/ggerganov/ggml). Xorbits Inference intelligently utilizes heterogeneous
hardware, including GPUs and CPUs, to accelerate your model inference tasks.

⚙️ **Flexible API and Interfaces**: Offer multiple interfaces for interacting
with your models, supporting OpenAI compatible RESTful API (including Function Calling API), RPC, CLI 
and WebUI for seamless model management and interaction.

🌐 **Distributed Deployment**: Excel in distributed deployment scenarios, 
allowing the seamless distribution of model inference across multiple devices or machines.

🔌 **Built-in Integration with Third-Party Libraries**: Xorbits Inference seamlessly integrates
with popular third-party libraries including [LangChain](https://python.langchain.com/docs/integrations/providers/xinference), [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/XinferenceLocalDeployment.html#i-run-pip-install-xinference-all-in-a-terminal-window), [Dify](https://docs.dify.ai/advanced/model-configuration/xinference), and [Chatbox](https://chatboxai.app/).

## Why Xinference
| Feature                                        | Xinference | FastChat | OpenLLM | RayLLM |
|------------------------------------------------|------------|----------|---------|--------|
| OpenAI-Compatible RESTful API                  | ✅ | ✅ | ✅ | ✅ |
| vLLM Integrations                              | ✅ | ✅ | ✅ | ✅ |
| More Inference Engines (GGML, TensorRT)        | ✅ | ❌ | ✅ | ✅ |
| More Platforms (CPU, Metal)                    | ✅ | ✅ | ❌ | ❌ |
| Multi-node Cluster Deployment                  | ✅ | ❌ | ❌ | ✅ |
| Image Models (Text-to-Image)                   | ✅ | ✅ | ❌ | ❌ |
| Text Embedding Models                          | ✅ | ❌ | ❌ | ❌ |
| Multimodal Models                              | ✅ | ❌ | ❌ | ❌ |
| Audio Models                                   | ✅ | ❌ | ❌ | ❌ |
| More OpenAI Functionalities (Function Calling) | ✅ | ❌ | ❌ | ❌ |

## Using Xinference

- **Self-hosting Xinference Community Edition</br>**
Quickly get Xinference running in your environment with this [starter guide](#getting-started).
Use our [documentation](https://inference.readthedocs.io/) for further references and more in-depth instructions.

- **Xinference for enterprise / organizations</br>**
We provide additional enterprise-centric features. [send us an email](mailto:info@xinference.co?subject=[GitHub]Business%20License%20Inquiry) to discuss enterprise needs. </br>

## Staying Ahead

Star Xinference on GitHub and be instantly notified of new releases.

![star-us](assets/stay_ahead.gif)

## Getting Started

* [Docs](https://inference.readthedocs.io/en/latest/index.html)
* [Built-in Models](https://inference.readthedocs.io/en/latest/models/builtin/index.html)
* [Custom Models](https://inference.readthedocs.io/en/latest/models/custom.html)
* [Deployment Docs](https://inference.readthedocs.io/en/latest/getting_started/using_xinference.html)

### Docker 

Nvidia GPU users can start Xinference server using [Xinference Docker Image](https://inference.readthedocs.io/en/latest/getting_started/using_docker_image.html). Prior to executing the installation command, ensure that both [Docker](https://docs.docker.com/get-docker/) and [CUDA](https://developer.nvidia.com/cuda-downloads) are set up on your system.

```bash
docker run --name xinference -d -p 9997:9997 -e XINFERENCE_HOME=/data -v </on/your/host>:/data --gpus all xprobe/xinference:latest xinference-local -H 0.0.0.0
```

### K8s via helm

Ensure that you have GPU support in your Kubernetes cluster, then install as follows.

```
# add repo
helm repo add xinference https://xorbitsai.github.io/xinference-helm-charts

# update indexes and query xinference versions
helm repo update xinference
helm search repo xinference/xinference --devel --versions

# install xinference
helm install xinference xinference/xinference -n xinference --version 0.0.1-v<xinference_release_version>
```

For more customized installation methods on K8s, please refer to the [documentation](https://inference.readthedocs.io/en/latest/getting_started/using_kubernetes.html).

### Quick Start

Install Xinference by using pip as follows. (For more options, see [Installation page](https://inference.readthedocs.io/en/latest/getting_started/installation.html).)

```bash
pip install "xinference[all]"
```

To start a local instance of Xinference, run the following command:

```bash
$ xinference-local
```

Once Xinference is running, there are multiple ways you can try it: via the web UI, via cURL,
 via the command line, or via the Xinference’s python client. Check out our [docs]( https://inference.readthedocs.io/en/latest/getting_started/using_xinference.html#run-xinference-locally) for the guide.

![web UI](assets/screenshot.png)

## Getting involved

| Platform                                                                                        | Purpose                                     |
|-------------------------------------------------------------------------------------------------|---------------------------------------------|
| [Github Issues](https://github.com/xorbitsai/inference/issues)                                  | Reporting bugs and filing feature requests. |
| [Discord](https://discord.gg/Xw9tszSkr5) | Collaborating with other Xinference users.  |
| [Telegram](https://t.me/+nCNpwmySwk9iYmI1)                                                       | Chatting with other Xinference users.       |
| [Twitter](https://twitter.com/xorbitsio)                                                        | Staying up-to-date on new features.         |

## Citation

If this work is helpful, please kindly cite as:

```bibtex
@inproceedings{lu2024xinference,
    title = "Xinference: Making Large Model Serving Easy",
    author = "Lu, Weizheng and Xiong, Lingfeng and Zhang, Feng and Qin, Xuye and Chen, Yueguo",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-demo.30",
    pages = "291--300",
}
```

## Contributors

<a href="https://github.com/xorbitsai/inference/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=xorbitsai/inference" />
</a>

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=xorbitsai/inference&type=Date)](https://star-history.com/#xorbitsai/inference&Date)
