Metadata-Version: 2.1
Name: xinference
Version: 1.2.2
Summary: Model Serving Made Easy
Home-page: https://github.com/xorbitsai/inference
Author: Qin Xuye
Author-email: qinxuye@xprobe.io
Maintainer: Qin Xuye
Maintainer-email: qinxuye@xprobe.io
License: Apache License 2.0
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development :: Libraries
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: all
Provides-Extra: intel
Provides-Extra: llama_cpp
Provides-Extra: transformers
Provides-Extra: vllm
Provides-Extra: sglang
Provides-Extra: mlx
Provides-Extra: embedding
Provides-Extra: rerank
Provides-Extra: image
Provides-Extra: video
Provides-Extra: audio
Provides-Extra: doc
Provides-Extra: benchmark
License-File: LICENSE

<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://inference.top/">Xinference Cloud</a> ·
  <a href="https://github.com/xorbitsai/enterprise-docs/blob/main/README.md">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)
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[![Discord](https://img.shields.io/badge/join_Discord-5462eb.svg?logo=discord&style=for-the-badge&logoColor=%23f5f5f5)](https://discord.gg/Xw9tszSkr5)
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<p align="center">
  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-454545?style=for-the-badge"></a>
  <a href="./README_zh_CN.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/中文介绍-d9d9d9?style=for-the-badge"></a>
  <a href="./README_ja_JP.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-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></i>
</div>

## 🔥 Hot Topics
### Framework Enhancements
- VLLM enhancement: Shared KV cache across multiple replicas: [#2732](https://github.com/xorbitsai/inference/pull/2732)
- Support Continuous batching for Transformers engine: [#1724](https://github.com/xorbitsai/inference/pull/1724)
- Support MLX backend for Apple Silicon chips: [#1765](https://github.com/xorbitsai/inference/pull/1765)
- Support specifying worker and GPU indexes for launching models: [#1195](https://github.com/xorbitsai/inference/pull/1195)
- Support SGLang backend: [#1161](https://github.com/xorbitsai/inference/pull/1161)
- Support LoRA for LLM and image models: [#1080](https://github.com/xorbitsai/inference/pull/1080)
- Support speech recognition model: [#929](https://github.com/xorbitsai/inference/pull/929)
- Metrics support: [#906](https://github.com/xorbitsai/inference/pull/906)
### New Models
- Built-in support for [DeepSeek-R1-Distill-Qwen](https://github.com/deepseek-ai/DeepSeek-R1?tab=readme-ov-file#deepseek-r1-distill-models): [#2781](https://github.com/xorbitsai/inference/pull/2781)
- Built-in support for [qwen2.5-vl](https://github.com/QwenLM/Qwen2.5-VL): [#2788](https://github.com/xorbitsai/inference/pull/2788)
- Built-in support for [internlm3-instruct](https://github.com/InternLM/InternLM): [#2789](https://github.com/xorbitsai/inference/pull/2789)
- Built-in support for [MeloTTS](https://github.com/myshell-ai/MeloTTS): [#2760](https://github.com/xorbitsai/inference/pull/2760)
- Built-in support for [CogAgent](https://github.com/THUDM/CogAgent): [#2740](https://github.com/xorbitsai/inference/pull/2740)
- Built-in support for [HunyuanVideo](https://github.com/Tencent/HunyuanVideo): [#2721](https://github.com/xorbitsai/inference/pull/2721)
- Built-in support for [HunyuanDiT](https://github.com/Tencent/HunyuanDiT): [#2727](https://github.com/xorbitsai/inference/pull/2727)
- Built-in support for [Macro-o1](https://github.com/AIDC-AI/Marco-o1): [#2749](https://github.com/xorbitsai/inference/pull/2749)
### Integrations
- [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 Base, it is a chatbot based on Large Language Models (LLM) and Retrieval-Augmented Generation (RAG). 
- [Chatbox](https://chatboxai.app/): a desktop client for multiple cutting-edge LLM models, available on Windows, Mac and Linux.


## 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

- **Cloud </br>**
We host a [Xinference Cloud](https://inference.top) service for anyone to try with zero setup. 

- **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:business@xprobe.io?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)
* [Examples and Tutorials](https://inference.readthedocs.io/en/latest/examples/index.html)

### Jupyter Notebook

The lightest way to experience Xinference is to try our [Jupyter Notebook on Google Colab](https://colab.research.google.com/github/xorbitsai/inference/blob/main/examples/Xinference_Quick_Start.ipynb).

### 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.  |
| [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)
